At RoboUniverse in San Diego, agricultural robots and the labor shortage were quickly identified as the biggest issues facing the industry today. Water scarcity and field health were other key issues mentioned, but it’s labor that keeps farmers up at night and robotics that could come to their rescue.

Current situation: Interim solutions

Labor availability and lack of fully effective automation have pushed some farming businesses to give up growing certain crops.

“We purposefully moved away from crops that require a lot of labor,” said Stuart Woolf, president and CEO of Woolf Farming & Processing, a Central Valley family farming group. “We now focus on tomatoes, almonds and other crops that can be mechanically harvested.”

However, pushing up the price of his fruit to pay for the increased labor costs is not a long-term solution, as Woolf said:

“I don’t want my commodity to become a luxury item that only a few can afford to eat. The labor issue will not get better, and we’re at a tipping point now, so we need to combine technology and labor to enable us to stay competitive; we can’t keep raising prices, it won’t work in the marketplace.”

Another California fruit farmer, Harold McClarty, president of HMC Farms, said:

“We won’t find a technology that picks fruit the way we do. I have yet to find a robot with an effective, dexterous arm. Instead we are developing certain things that we can use in the packing houses. There are lots of modifications that can be made in the packing houses; they’re a much better and easier place to innovate.”

At RoboUniverse, in a track entitled The Intersection of Technology and Applications in Agriculture coordinated by ag tech startup Food Origins CEO Nathan Dorn, participants were presented with robotics-related technological opportunities from computer vision to AI and from soft and hard gripping to intelligent path planning. There was also a panel where farm managers described their needs and impatience waiting for technology to effectively perform.

[Food Origins is developing sensing technology and analytics that allows it to gather and parse data in new and disruptive ways tracking value from harvest to table, allowing transparency and traceability.]

Dino Giacomazzi of Giacomazzi Dairy Farms said, “Our workforce is either 25 years old or 65 years old. There is no in between right now and that is a problem. The 25-year-old farm hands either don’t like the work or find better work and are gone after payday. I have living animals who need the engagement from good people or good machines.”

In June, The Green Economy quoted Kip Tom, a 7th generation Indiana farmer who heads Tom Farms, an enterprise that encompasses 20,000 acres of corn and soybean across 7 Indiana counties and is also one of Monsanto’s largest seed producers, who said:

“Farmers that don’t learn about these new ag technologies will get left behind. Whether it’s information and technology, or equipment technology, there are technologies that will help our industry deliver a sustainable, affordable, and nutritious supply of food to global consumers. It’s exciting, but what is most exciting is when we see collaboration between people in the field, who understand what we need, and developers in places like Silicon Valley and the investment community saying this is something new that we haven’t paid much attention to.”

Brian Antle, of Tanimura & Antle (T&A), built a 600-unit housing facility in Spreckels, California, to house temporary agricultural workers with H2A visas. T&A has also invested in many ag tech startups. Antle said that it was no longer an option to sit on the sidelines and wait for machinery builders to deliver solutions.

“We are all losing opportunities to sell quality products because the lettuce is picked late or we are too short staffed to meet our goals. If we cannot get more people, we must find machines.”

Trends and forecasts

Data generated by sensors or drones and collected by farms on the field or during field operations offer a wealth of information about soil, seeds, livestock, crops, costs, farm equipment and the use of water and fertilizer. Low-cost Internet of Things (IoT) technologies and advanced analytics are already beginning to help farmers analyze data like weather, temperature, moisture, prices, and communication signals, and provide insights on how to optimize and increase yield, improve farm planning, make smarter decisions about the level of resources needed, and determine when and where to distribute those resources in order to prevent waste and increase yield.

Efficiency and productivity will increase in the next few years as precision agricultural methods become more prevalent and farms become smarter and more connected, but this could just be the precursor to even greater use of robotic technology in farming.

BusinessInsider Intelligence predicts that IoT device installations in the agriculture world will increase from 30 million units in 2015 to 75 million in 2020, for a compound annual growth rate of 20%.

OnFarm, a startup that makes an IoT platform, ran studies and determined that for the average farm, yield rose by 1.75%, energy costs dropped $7 to $13 per acre, and water use for irrigation fell by 8% when IoT devices streamed their data to software that analyzed all the input sources and output meaningful farm management prescriptions.

A new report by Tractica, a Colorado research firm, forecasts that shipments of agricultural robots will increase significantly in the years ahead, rising from 32,000 units in 2016 to 594,000 units annually in 2024, by which time the market is expected to reach $74.1 billion in annual revenue.

Robotics companies are keenly focused on the agricultural market opportunity. The Tractica report examined 178 industry participants – a particularly large number – who are developing and launching robotic systems to address the need for more automation to improve efficiency, reduce costs, and address labor concerns in the agriculture market.

Tractica’s report, “Agricultural Robots”, developed in collaboration with The Robot Report, examines global market trends for agricultural robots and provides 10-year market sizing and forecasts for agricultural robot shipments and revenue during the period from 2015 through 2024.  The report examines the market opportunities, barriers, and technology issues for each of the key application markets.  Market forecasts are segmented by world region and application type.  The study also includes 178 profiles of industry players in the agricultural robot market.

If you enjoyed this article about agricultural robotics, you may also want to read:

• How regenerative agriculture and robotics can benefit each other
• Swarms of precision agriculture robots could help put food on the table
• Top 10 technologies in precision agriculture
• How AR technology can help farmers stay relevant

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

I’ve come around to the view that the best and most inclusive term for high-concept farming which is both sustainably productive and ecologically responsible is Regenerative Agriculture. It implies all that is meant by permaculture, agroecology, carbon farming, and organic farming, but goes beyond these to focus on living matter in the soil, and in this is closely aligned with the term biodynamic. That said, I’m not prepared to argue the point; I only say this by way of explaining why I’ve chosen to use this term here.

These are perilous times, perhaps not quite to the extent portrayed by tabloid journalism, and which might be suggested by a recent abundance of misinformation and misinformed opinion on social media, but perilous nonetheless, and there are plenty of squeaky wheels around all competing for grease. In this political-economic environment, making a case for adequate funding (whether public or private) has become increasingly difficult. The cost-benefit ratio, or ‘bang for the buck’, is again the primary metric driving investment, with benefit usually being measured in terms of potential profit where private investment is concerned.

The synergy of substantive collaboration (or ‘co-leverage’) can be all-important in determining which projects get funded. If you can show that advances in your field are synergistic with advances in another field, such that each furthers the goals of the other, and your proposal crosses those boundaries, you stand a better chance of attracting investment than if you go it alone.

I believe such a synergy exists, or could easily be made to exist, between robotics and regenerative agriculture.

Robotics needs a market into which it can sell a large volume of machinery of many kinds, and from which it can gain abundant experience, without the liability burden presented by autonomous vehicles operating on streets and highways. Agriculture is good for this, in terms of scale, tolerance for beta-ware, and the opportunity for gaining experience with new approaches and new technologies, as well as with operating within complex environments. Regenerative agriculture is particularly ideal, because the need is for small-scale, context-sensitive equipment, which, if it malfunctions, will only cause minimal damage, and the complexity of the environments involved is at least an order of magnitude greater than with conventional agriculture.

Regenerative agriculture needs a way of scaling up its best practices, so they can be deployed across millions of hectares, without resorting to compromises involving heavy equipment and soil compression, or herbicides, and without waiting for millions of people to decide that they want to go back to the land and work it by hand. Because many of those best practices involve attention to detail, that scalability can only be achieved through robotics. On the other hand, given equipment that automates not only the work of regenerative agriculture but data collection, the stage would be set for pushing the state of the art, to evolve even better practices and to further develop crop genomes based on phenotype (how plants actually perform under diverse conditions).

Together, robotics and regenerative agriculture can do much to drive each other’s development, and to improve humankind’s prospects for the future. But, of course, the near-term bottom line is that, together, they can make a more compelling case for adequate funding.

If you enjoyed this article, you may also want to read:

• How AR technology can help farmers stay relevant
• Robohub roundtable: Robotic bee swarms from Black Mirror – what’s hype, what’s real?
• Drought and desertification: How robots might help
• Keeping a robotic eye on pollution
• Swarms of precision agriculture robots could help put food on the table
• Top 10 technologies in precision agriculture

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Image: Wheat Genetics and Germplasm Improvement

I’ve long believed that Augmented Reality (AR) and robotics are closely related. Both model their environments to some degree. Robotics uses that model to guide the behavior of a machine, whereas AR uses it to provide an enhanced sensory experience to a human.

The exact nature of that enhanced experience is bounded only by available sensory, computational, and display (audio, haptic, …) hardware, and by how the data gathered can be usefully transformed into overlays that augment the natural perception of the human user. What is useful is a function of both the content of those overlays and the latency, how much lag time is introduced by the computations involved in generating the overlays. Faster computational hardware can produce more detailed overlays with the same latency or the same overlays with lower latency than slower hardware.

One important application for AR is making it safer and easier for a human to work in collaboration with robotic hardware. For example, a robot might provide the path it intends to follow and the 3D space through which it intends to pass, and that information might be converted in an AR display into highlighting of anything occupying that space. Or perhaps a machine wants to direct the attention of its human counterpart to some particular element of the environment, say one specific plant. That too could be highlighted in the display.

While these examples only scratch the surface of what is possible, they do serve to illustrate that the content of the AR overlays need not be generated entirely from data gathered by sensors attached to the display itself, but can be provided by other sources, including but not limited to other nearby devices. Those sources might include aerial or satellite imagery and information from databases. In the farming context, they might include 3D soil maps produced from core samples.

Examples of overlays that might be useful for a farmer include thermal imagery, current soil moisture content, soil surface porosity and water absorption capacity, exaggerated vertical relief and what to expect in the way of runoff and resulting erosion for various precipitation scenarios, highlighting all plants of a particular species, all plants exhibiting nutrient deficiencies or other trauma, highlighting bare soil (no mulch or plant cover), the presence, activity, and impact of various types of animals. This list could go on and on.

Machines may be better at doing particular manipulations of data, finding correlations, and even at answering well-specified questions, but they’re not so good at asking meaningful questions, much less at thinking outside the box. For this reason, the combination of human and machine is more powerful than either alone.

It’s still very early days in AR, and there’s a great deal of room for improvement. One development that is likely to occur sooner rather than later is voice operation, enabling hands-free control of the AR experience, including which overlays are active and how they are combined. With voice control, a farmer should be able to walk through a field, say what he wants to see, and make modifications to the plan controlling the robotic machinery that actually operates the farm, or issuing commands for execution by the first available machine. For most, this will be a more intimate and far richer connection to their land than what they currently experience.

If you enjoyed this article, you may also want to read:

• Robohub roundtable: Robotic bee swarms from Black Mirror – what’s hype, what’s real?
• Drought and desertification: How robots might help
• Rising need for nursery, indoor and vertical farming
• Keeping a robotic eye on pollution
• Swarms of precision agriculture robots could help put food on the table
• Top 10 technologies in precision agriculture

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

A plant-moving robot from Billerica-based Harvest Automation. Source: harvestai/YouTube

To meet rising food demands from a growing global population, over 250 million acres of arable land will be needed – about 20% more land than all of Brazil. Alternatively, agricultural production will need to be more productive and more sustainable using our present acreage. Meeting future needs requires investment in alternative practices such as urban and vertical farming as well as existing indoor and covered methods.

Ray Kurzweil, futurist, inventor and Google’s Director of Engineering, said in an interview in The Times in 2013:

There will be a new vertical agriculture revolution, because right now we use up a third of the usable land of the world to produce food, which is very inefficient. Instead we will grow food in a computerized vertical factory building (which is a more efficient use of real estate) controlled by artificial intelligence, which recycles all of the nutrients so there’s no environmental impact at all.

Fully automated regional vertical farms for leafy greens and other commodity crops has long been a vision of the future. Capital costs and other vagaries have prevented such development to date, but lower costs for technology and automation plus higher costs for labor, land and other resources, are making Kurzweil’s predictions come true. There are dozens of vertical farms around the world today with more being built.

Spread, a Japanese factory farmer with a large facility near Kyoto that serves the two metropolitan areas of Kyoto and Osaka, is nearing completion of a fully automated 52,000 sq ft facility where 98% of water will be recycled and seeding, watering, applying fertilizer and harvesting will all be automated. No earth; just shelves on top of shelves from floor to ceiling. They predict 30,000 heads of lettuce can be harvested and delivered daily throughout the year.

Propelling this indoor and vertical farming movement are three influential trends. The Boston Consulting Group, in 2015, produced a study entitled “Crop Farming 2030, the Reinvention of the Sector,” and cited (1) the steady global movement toward precision farming, (2) the availability of economical automation and robotics, and (3) the growing labor shortage as the drivers of the movement.

Vertical farming:

Food grown year round in buildings near urban centers provides many advantages: being close to the point of consumption reduces both distribution costs and spoilage. Outdoor farming is vulnerable to pests and disease, which in turn means intensive use of pesticides and herbicides causing problems with runoff as well as food safety. Vertical farms protect crops from weather and pests and reduce or eliminate the use of pesticides and herbicides. Hydroponic and aeroponic water methods save massive amounts of water compared to outdoor farming. Consequently, as these farms become more prevalent, they could provide a major new role for the ag industry to produce a wide range of commercial crops with major savings in space and water use. In the case of Spread, cited above, they are able to grow lettuce indoors using less than 1% of the water that California Central Valley growers use to grow the same product!

Agriculture accounts for around 70% of water used in the world today according to the OECD (Organization for Economic Co-operation and Development). As population and climate change progress, food needs will grow, and more efficient use of water in ag must happen as well. Vertical farms reduce water usage through recirculating hydroponics, evaporative cooling, control of in- and out-airflow, and other methods. Urban Crops, a Belgian factory farmer and technology provider uses this chart to show the benefits of vertical factory farms versus other methods:

Greenhouse and wholesale nurseries

Greenhouse technology is ideal to protect plants from adverse climatic conditions, insects and disease and to nurse, propagate and grow plants to usable and/or harvestable size. Greenhouses can be framed or inflated structures covered with glass or transparent or translucent material. Greenhouse yields are often 10-15% greater that outdoor yields, consistency and quality tend to be greater, and the growing season is longer.

Similar to vertical farms, greenhouses have high upfront costs and operating expenses, and crop selection must not require pollination. Whether plants are grown in the field or indoors, nurseries transplant, graft, or germinate plants to create seedlings for resale. Their processes are quite complex on two levels: (1) the technical aspects of growing plants which require management of the environment, plant nutrition, propagation, transplanting, irrigation, and pest and disease control, and (2) the business aspects of managing production, labor, customers, distribution and other activities associated with a business. Many nurseries use automation and some level of robotics. Harvest Automation and their mobile robots rearranging potted plants and Urbinati and Visser and their robotic transplanting devices are all examples of the levels of automation utilized in nursery operations.

Commercial and emerging providers:

• AeroFarms – A NJ indoor farmer that is marketing their technology to other prospective vertical farmers. AeroFarms grows a wide variety of leafy greens without sun or soil in a fully-controlled indoor environment using a system of aeroponic misting of the roots for faster harvest cycles, predictable results, food safety and less environmental impact.
• Aris – is a Dutch engineering and systems provider. Many of their projects are integrated with vision and robotics that identify, grade, sort and analyze everything from orchids to chickens, from potted plants to seedlings. Using their systems, nursery clients can then grade and robotically cut branches which can then be potted.
• ALCI Visionics & Robotics – A French integrator of vision and robotics technologies for meat and fish slicing and packaging and for nurseries and growers for potting plants and seed germination, analysis and classification for corn, rice and wheat.
• Conic Systems – a Spanish provider of greenhouse equipment including robotic and software-controlled grafting, seeding and planting systems.
• Demtec – A long established Belgium-based maker of a wide range of horticultural machinery including potting machines, seeders, planters and transplanters. Many of these processes have integrated industrial and mobile robots into their systems. Demtec robotics also play a big part in flat and shelf handling, packaging, palletization, and shipping.
• Egatec A/S – a Danish integrator of end-of-line packaging, boxing and palletizing systems for the ag and food processing industries.
• Harvest Automation – a Boston-area mobile robotics provider with nursery applications for spacing, a task that involves bending over, picking up one or two containers often weighing up to 22 pounds each, walking a few steps and then bending over again to place them in a predefined pattern.  The company recently divested a warehousing variation on their mobile robot to better focus on ag industry applications.
• Helper Robotech – a Korean manufacturer of robotic grafting, smart seeding and other smart devices. They also make a wide range of nursery products to nurture seedlings to maturity.
• HETO Agrotechnics – a Dutch manufacturer of horticulture machines including robotic potting systems and pick and place systems for potted plants.
• Hortiplan – a Belgian integrator, reseller and provider of nursery equipment, supplies and mobile gully systems – which move in an automated way from the planting side to the harvesting station. Hortiplan also designs and sells lighting, irrigation and handling systems.
• Irmato Jentjens – An established Dutch builder of systems for automating food handling and packaging. Irmato also makes the Rombomatic, a robotic cutting system for nurseries that examines, assesses, cuts, powders and inserts cuttings into pots and other mediums. Jentjens is a funding partner in a variety of sensing and manipulation projects under the EU’s Clever Robots for Crops program. These include a sweet-pepper harvesting robot, an apple harvesting robot, precision and canopy-optimized spraying robots and other AI-based ag systems.
• Iron Ox – A Silicon Valley startup presently in stealth mode but hiring with a plan to provide a fully robotic, fully controlled environment for ag in a greenhouse growing leafy greens (lettuce, basil and bok choy) using natural light but mobile bots to move plants through each stage of development to harvesting, packaging and palletizing.
• ISO Group – a Netherlands-based supplier of automation solutions for nurseries. They adapt industrial robot technology for horticulture uses such as grafting, planting, vision inspection and replanting.
• Logiqs BV – a Dutch manufacturer of internal transport and logistics systems for greenhouses for growers of cut flowers, tree nurseries, flower bulbs, potted plants and vegetables. Their new modular GreenCube vertical cultivation system uses trays sensors and vertical transporters, also with sensors, for movement between layers and movement to and from the various stages of nursery growing operations.
• CMW Horticulture – a UK integrator and reseller of a whole range of greenhouse and nursery automation products including Logiqs mobility and handling systems
• Mirai Group – A Japanese farmer that, in 2009, became a member and leader of the Japanese government public-private consortium to develop low-cost plant factories. Today Mirai provides R&D and design-build services to grow leafy plants for farmers interested in vertical farming similar to what Spread is doing. Mirai is also producing and wholesaling leafy vegetables from a large plant factory located near Chiba, Japan.
• Photon Systems Instruments – An established Czech Republic provider of ag instruments including high throughput conveyor and robotic nursery phenotyping systems.
• Priva Group – a Dutch engineering, design and systems integrator for greenhouse nurseries. Recent projects include developing a leaf-removing robot for tomato plants. The lowest leaves of tomato plants are regularly removed to promote ripening (this process is called de-leafing).
• QUBIT Phenomics – a Canadian provider of conventional and robotic plant screening systems for nurseries and growers. The company’s PlantScreen Field Phenotyping System allows growers an automated non-invasive measurement of photosynthesis, leaf biochemical status, water status and canopy temperature. Greenhouses are the primary marketplace but the company hopes to break into field operations as well.  Many major biotech companies and universities have partnered with Qubit in the study of plant responses to various stresses.
• Spread – A Japanese lettuce grower, is constructing the world’s largest plant factory near Osaka and Kyoto. The new factory, scheduled for mid-2017, will be as robotically automated as possible. Spread’s existing facility, from which they are learning what tasks can be automated, produces 21,000 heads of lettuce per day using LED lighting, controlled air conditioning and recirculating water. Spread is planning to construct and operate 20 new factories in the next 5 years in addition to selling the technology for others to build their own plant factories.
• Urban Crops – A Belgium startup pioneering the distribution of robotized vertical farming and plant factories.  The company offers two types of products, one fits into a 40’ container and can be fully automated or not, and another is custom built for larger spaces. Currently the company has made a small number of sales and has partnered with companies such as Belgocatering, a Belguim based catering company, and a UAE group of investors.
• Urbinati – an Italian manufacturer of nursery technology including automated, and in some cases robotic, seeding, pot filling, transplanting, handling and irrigation devices. They also sell backroom processing robots such as palletizers.
• Transplant Systems – a NZ integrator and reseller of nursery machinery and robots from Urbinati and others.
• Visser – a Dutch provider of horticulture automation systems and complete production lines for large and small nurseries and greenhouses including a robot seeder, transplanter and packing and palletizing robots.

Agricultural Robotics: 160+ profiles

Working together with Tractica, a Colorado research firm, my team and I compiled a list of over 200 global businesses and agencies involved in developing robotic solutions for the ag industry. From that list, I was able to interview and profile over 160 companies and 16 research labs as follows:

• Academic and research labs (16)
• Backroom and post processing (5)
• Dairy and milking (10)
• Drones, analytics and data service providers (26)
• Farm equipment manufacturers (23)
• Harvesting, weeding and thinning robots (21)
• Hobby farming (2)
• Indoor and vertical farming (23)
• Integrator, distributor and reseller (20)
• Self-driving vehicles (15)
• UAS/UAV vendors (15)

This research report will be published in the next few weeks and will contain the whole list, the profiles, and the conclusions drawn from the research, interviews and analyses. The report will be $4,200 for a Basic License (1-5 users) or $6,300 for an Enterprise License.

Note: the link to the report is to the previous report with the same title and will be updated with new information just as soon as the new report is published.

Source: Nesta.org, Precision Agriculture

Farmers, ranchers and growers the world over are transitioning to precision agricultural methods, i.e., subdividing their acreage into many unique sub-plots — in some cases right down to the individual plant, tree, or animal — thereby enabling increased productivity, trace-ability and lower overall costs. Low-cost aerial vehicles, sensors and cameras are integral to the process and are being used to map, observe, sense and spray.

Digital farms are already a reality

Robotic automation is already widely practiced and can be seen today in milking systems and increasingly in precision techniques that use sensors and drone-mounted cameras to steer tractors and to monitor soil for temperature, moisture, disease, varmints, crop quantity, weather damage, and nutrient content. This data is then analyzed in order to improve decision-making on planting, weeding, pruning and chemical application. Digitally-controlled farm implements are already in use in developed countries and most Western farmers and ranchers are high-tech to some extent and more variable-rate dispensing devices are on the horizon. Self-steering kits can be found in most new tractors and follow RTK/GPS and digital guidance with levels of accuracy down to the centimeter level.

There are partially and fully automatic and robotic devices for most aspects of agricultural functions from grafting to planting, from harvesting to sorting, from packaging to boxing, but because the metrics of their implementation have thus far been too costly, and the safety and liability aspects difficult, so they are not widely deployed. These challenges are being met with a new group of vendors, lower costs, and new robotic products, often marketed in the form of Robotics-as-a-Service (RaaS). Adding to these challenges are ever-increasing costs and lack of available water, labor and tillable land.

I observed an example of those additional challenges on a recent research trip to Odense, Denmark.

EGATEC A/S specializes in automated end-of-line packaging and palletizing solutions. They are an integrator. Rosborg Denmark is a nursery that grows and sells miniature flowerpots and spices. Rosborg is one of EGATEC’s clients and the two are working together to automate Rosborg’s end-of-line packaging process – a step that presently handles 12 million plants per year and which is done entirely by hand by 40-60 people.

Rosborg is already mechanized. They’ve automated their potting and seeding operations. Plants then are placed on mobile trays slowly moving towards the harvesting stations where fully-grown plants are picked, wrapped and packaged for boxing and palletizing. These latter steps are done by hand and are the steps being automated by EGATEC.

Many of Rosborg’s employees are immigrants eager for the work even though, by Danish standards, it is low paying. In Denmark non-citizens are subject to the same mixture of union and other agreements as citizens and thus their hourly wage approximates $20 an hour — quite high and easy for a robot packaging cell to beat.

Transformative effect on agricultural automation

Unmanned aerial vehicles (UAVs) (drones) are stimulating this transformative effect on agricultural automation. UAVs are beginning to be used in areas as diverse as soil and field analysis, planting, crop spraying and monitoring, irrigation and plant and crop health assessment. They are producing valuable data enabling farmers to make better decisions.

However, amid the current shift toward precision agricultural practices, farmers are also running into hurdles with technology integration. Farmers often have to mix precision ag hardware and software with third-party vendors; the results can lead to lost time from a variety of reasons. To combat these barriers, many farm equipment conglomerates are upgrading their products and offering consolidation software. For example, Trimble Ag Software promises to seamlessly integrate with its hardware solutions as well as with other third-party manufacturers using Trimble’s API. By using one technology platform, data can flow wirelessly across the entire farm. This provides timely crop input recommendations and eliminates data re-entry, saving time and reducing potential errors.

Trimble’s Ag Software, like many others, is attempting to be an all-in-one tool offering customers a complete desktop, web-based and mobile-enabled agricultural software solution for farmers and their stakeholders: farm managers, supervisors, crop advisors, service providers, ag retailers and food processors.

Agricultural Robotics: 149 Profiles

Working together with Tractica, a Colorado research firm, my team and I compiled a list of over 200 global businesses and agencies involved in developing robotic solutions for the ag industry. From that list, I was able to interview and profile over 149 companies and 16 research labs as follows:

• Academic and research labs     (16)
• Backroom and post processing     (5)
• Dairy and milking     (10)
• Drones, analytics and data service providers     (26)
• Farm equipment manufacturers     (13)
• Harvesting, weeding and thinning robots     (21)
• Hobby farming     (2)
• Indoor and vertical farming     (23)
• Integrator, distributor and reseller     (20)
• Self-driving vehicles     (15)
• UAS/UAV vendors     (14)

Source: tractica.com

This research report will be published in the next few weeks and will contain the whole list, the profiles, and the conclusions drawn from the research, interviews and analyses. The report will be $4,200 for a Basic License (1-5 users) or $6,300 for an Enterprise License. Note: the link to the report is to the previous report and will be updated with new information just as soon as the report is published.

If you enjoyed this article, you may also enjoy these articles about agricultural robotics:

• Swarms of precision agriculture robots could help put food on the table
• Agricultural robot market anticipated to reach $16.3 billion by 2020
• Top 10 technologies in precision agriculture
• Robohub Digest 09/16: AI100, RoboLaw, sailing and farming robots
• In agriculture, robots replace job vacancies

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Drone flying over a field.

by Fintan Burke

The last place you might expect to find drones and rovers is checking up on a corn field, but they could soon join tractors and ploughs on a farmer’s list of must-have agricultural tools, thanks to their potential to reduce pesticide use and increase the amount of crops that can be grown.

It’s part of a move towards so-called precision farming, where farmers use equipment to monitor their crops and respond to issues when and where they occur, rather than applying blanket solutions to an entire field.

The Issue
The dangers of excessive pesticide use on the environment can be traced back to the 1962 book ‘Silent Spring’ by Rachel Carson, which has been credited as starting the modern conservation movement.
The EU now applies restrictions to certain pesticides containing neonicotinoids, which have been linked with the collapse of honey bee populations throughout Europe.
In 2014, an EU study highlighted the benefits of precision farming. Using this method can reduce pesticide and fertiliser runoff, which can greatly damage the local environment

Dr José M. Peña of the Institute for Sustainable Agriculture in Cordoba, Spain, ran the EU-funded TOAS project, in which drones were flown over crops to create weed infestation maps for farmers so they could pinpoint which areas to treat.

‘In Europe, 60 % of the pesticides we apply are herbicides to control the weeds,’ said Dr Peña. ‘(With) the technology applied in the crop we can radically reduce the use of these chemicals, and that is a benefit for the environment and the farmer.’

Dr Peña and his team used drones and image analysis technology, which detects slight differences in field colour, to discover vegetation and competitive weeds. However, weeds and budding crops can appear similar in the early season, when crops are at their most vulnerable.

The solution involved examining crop patterns alongside images. ‘We know the crop follows a pattern, and the vegetation that is out of this pattern is classified as weeds,’ said Dr Peña.

‘So in this way we can integrate the spectral information (and) also the position and the shape of the plants in the software to detect those ones that are weeds.’

Farmers can then apply weedkiller to specific areas, monitor the evolution of their crops over time, and create a photorealistic 3D map of their field by taking aerial pictures from multiple views.

Researchers programmed drones to recognise weeds outside normal crop patterns. Image courtesy of TOAS

Maize, olives

After firstly analysing maize fields and olive groves, the team later found the technology worked for other crops such as sunflowers, almonds and grapes.

‘This demonstrates that the technology and the project can go further than the original idea,’ said Dr Peña. ‘As well, we attained a very high accuracy – we can detect more than 95 % of the weeds in the fields. One important thing is that we can detect the parts of the crop field where it’s free of infestation. This place is the part where the farmer does not have to apply the herbicide.’

Spotting the weeds is just one half of the task, though, and researchers elsewhere are developing unmanned ground vehicles, or rovers, that can complete the job by getting rid of the weeds.

“We can radically reduce the use of these chemicals, and that is a benefit for the environment and the farmer.” Dr José M. Peña, Institute for Sustainable Agriculture, Spain.

‘What we are doing is really demonstrating that it can be done fully automatically,’ said Prof. Roland Siegwart of ETH Zurich, Switzerland, who leads an EU-funded robotics project called FLOURISH to develop a prototype of an integrated drone and rover system.

By scanning different crop characteristics, such as height and canopy cover, the drone can communicate the areas that need attention to the unmanned rover on the ground. The rover then removes the weeds, applies pesticide to a specific area, or highlights areas that may need extra fertiliser to the farmer.

Prof. Siegwart says that this could lead to much more efficient use of pesticides.

‘I would estimate it’s probably a very small fraction of a percent of all chemicals which are sprayed on the field which really has an impact,’ he said. ‘If we have more information you can actually, hopefully, get to a much higher efficiency. With probably 100 times less chemistry put on the field you have exactly the same effect.’

Increased research in agricultural technology may also have the added benefit of removing some stigma about drone technology itself as it helps to solve problems such as how to produce enough food for a growing population.

‘Typically we say that around 20-30 % of the whole collection of food on the field is already lost because there are some issues on the field. If you can reduce this it can really help to feed the world’s society.’

Source: Clearpath Robotics

Tell me a little bit about CEAR Lab.

AD: I founded the CEAR Lab about four years ago after finishing my PhD and post doc at Carnegie Mellon University in Pittsburgh. I came back to the Technion in Israel and started the Civil, Environmental, Agricultural Robotics lab in the Faculty of Civil and Environmental Engineering. We work on soft robots, dynamic robots, and optimization of manipulators, mostly with civil applications, a lot of them related to agriculture. Other applications are search and rescue, automation in construction and environmental related work as well. But, generally, we’re mostly focused on agriculture robotics and robotic systems in the open field.

How did the apple harvesting robot project come to… fruition?

AD: One of my PhD students is doing more theoretical work on task-based design of manipulators. Because cost is very important in agriculture, we’re trying to reduce price and find the optimal robot to do specific tasks. We’re actually seeing that different tasks, although they look very similar to us- apple picking or orange picking or peach picking – are actually very different if you look at the robot’s kinematics. We might need different joints, different lengths and so on. So we’re collecting data and modeling trees, we’re doing optimization and we’re finding the optimal robot for a specific task. This is something we have been working on for a few years. As part of this work, we are not only designing the optimal robot, but are also looking at designing the tree – finding the optimal tree in order to further simplify the robot.

There is a new Israeli start-up called FFRobotics (Fresh Fruit Robotics or FFR). Avi, who is the CEO and Co-Founder, approached us a few years ago after hearing one of my students give a talk on optimization of a tasked-based harvesting manipulator. FFR are building a simple robotic arm – a three degree of freedom Cartesian robot, with the goal of having 8 or 12 of these arms picking apples (or other fruits) simultaneously. We were helping them with the arm’s optimization. We started collaborating and then a few months ago, Microsoft approached us and asked us to exhibit at their Think Next exhibition that they have every year. So we decided to put one of the arms on our Grizzly RUV robot which fit pretty nicely. We brought those to the demo, along with a few Jackals and had a pretty good time!

Tell me a bit about how the robot works and its components.

AD: So the FFR arm has a vision system to segment and detect the apples. After it finds an apple, it gives the controller the XYZ location and the arm then reaches for the fruit. It has an underactuated gripper at the end with a single motor which grips the apple and rotates it about 90 degrees to detach it. The arm doesn’t go back to the home position, it just retracts a bit, lets go and the apples go into a container. In our lab, we are concentrating right now on the automation of the mobile robots themselves – on the Grizzly, Husky, and Jackals – we have a few of your robots. Fruit Fresh Robotics is working primarily on the design of the manipulator.

What kind of vision sensors are being used?

AD: On the Grizzly, we have an IMU, SICK LIDAR in Front, a stereo camera on a Pant-Tilt unit, and a dGPS. The arm uses a Kinect to do the sensing, but FFR is also looking into time of flight sensors to provide better robustness in the field.

Robot prepares to pick the next apple off the tree at Microsoft’s Think Next exhibition. Source: Clearpath Robotics

What would you say is the most challenging part of automating apple picking?

AD: I believe that the most difficult thing is making something cheap and robust. I think the best way is to not only think of the robot but also to train the trees to be simpler. Now this has been done in the past decade or two to simplify human harvesting. In extreme cases, you can see fruiting walls that are nearly planar. They did this to make it a bit simpler for human harvesters. This is even more important for robotic harvesting. Trying to get a robot to harvest apples on a totally natural tree is extremely complex, and you will most likely need a 6-7 degree of freedom robot to do it. In terms of vision, perception will be very difficult. People have tried it in the past and it just doesn’t make sense and makes everything too expensive for farmers to use.

By taking simpler trees, ones that were trained and pruned as the ones in our collaboration with Fresh Fruit Robotics, you can actually use a three degree of freedom robot – the simplest Cartesian robot – to do the picking. But, I think you can even go further to make the tree in a more optimal shape for a robot, let’s say a quarter of a circle. This may not be good for a human, but might be perfect for a robot and perhaps will allow us to use simpler robots that only need two degrees of freedom. So, making the system robust while keeping costs down is the hardest part and in order to do that you have to work on the tree as well.

How exactly do you train a tree?

AD: Training systems such as a fruiting-wall require high density planting while ensuring that the trunk and branches are not too thick. In order to do that you have to support them with a trellis and other engineered support system. You want to make them optimal so that all the energy and water goes mostly to the fruit and not the tree itself. This has been done for a while now, and we are essentially piggy backing on that. The simplification of trees may be even more important for robotics than for humans, if we want robots to go into fruit picking and harvesting.

Can the robots autonomously navigate and patrol the apple orchards?

AD: Not at the moment. We are in the process of doing it now on all of our robots. Right now we are trying to do full SLAM on an orchard in order to do patrolling for harvesting. This is the goal we are aiming for this summer.

How do you compensate for weather effects?

AD: In Israel the weather is relatively mild, so the problem is usually with sun and wind rather than snow and rain. The main problem the weather creates is with the perception of the robot, having to compensate for changes in light and in position of the objects. To overcome this FFR uses a cover, like a tent, to shield the tree and the robot. If it’s windy, you have to use fast closed-loop control because if the target starts moving after it’s been perceived, the system has to keep on tracking it in order for the gripper to accurately grip the object where it is and not where it was 10 seconds ago.

Why did you choose the Grizzly RUV for this project?

AD: We’ve had the Husky UGV for a while. We also have some older robots from 10 years ago, such as a tractor that we modified to be semi-autonomous. But, none of these vehicles were strong enough to actually do real work while being easily controllable. We wanted to carry bins full of fruit which weigh more than half a ton and we didn’t have a robot that could do that. Also, I wanted to have something that my students could take and apply the algorithms to a real-sized robot that could work in the field. We started with a TurtleBot to learn ROS, and then moved to the Husky. Then I wanted something I could just scale up and actually move to the field. That’s what we’re doing right now. It’s pretty new, so we’re in the early stages of working with it.

How did you find the integration process?

AD: Mechanical integration was very easy. We have not yet fully completed the electrical integration – instead we have two separate controllers and two separate electrical systems. Later on it will be relatively easy for the electrical system to be plugged into the robot since it uses the same voltage more or less. But right now it is decoupled.

Are you at a point where you can quantify the efficiency and cost benefits of the robots compared manual picking?

AK: We designed the system to pick approximately 10,000 high quality fruits per hour – that is without damaging the fruit while picking. Assuming working during the day only, the robot could potentially save farmers 25% in harvesting costs. It is important to understand that the same system will be able to pick other fresh fruits by replacing the end effector (gripper) and the software module. This option to harvest multiple fruits types increases the efficiency of the system dramatically.

What was it like to demo the robot to Satya Nadella, CEO of Microsoft, at the Think Next conference?

AD: It was exciting! He didn’t have a lot of time to spend at our booth. But it was an exciting exhibition with many demonstrators. We had the biggest robot for sure. It was fun! The FFR arm picked apples from the tree, and dropped them in the Jackal. Then, we drove the Jackal around delivering picked apples to people and they liked it. For safety reasons, we didn’t move the Grizzly. We just parked it and it didn’t move a centimeter the whole time. It was fun bringing such a big robot to the demo.

Picked apples were placed in a basket on top of a Jackal UGV and delivered to people in the crowd at Microsoft’s Think Next exhibition. Source: Clearpath Robotics

How close are you to a commercial release?

AK: Following last year’s field tests we believe the commercial system is about two years ahead. During the summer of 2016 we plan to test a full integrated system in the apple orchard, picking fruits from the trees all the way to the fruit bin. The first commercial system will be available for the 2017 apple picking season.

What is next for CEAR lab?

AD: We will continue working on the theoretical part of the optimization of the robotic arms. We’re looking into new ideas on re-configurability of arms – having arms doing different tasks and pretty easily switching from one task to another. With the Grizzly, we’re working on autonomous navigation of the apple orchards and are also working on a non-agricultural project related to search and rescue. We designed a suspension system on the Grizzly and mounted a stretcher on top of it. The motivation is to be able to evacuate wounded from harm’s way autonomously in rough terrain.

We are pretty happy with our robots. It’s a big family! We pretty much have all of Clearpath’s robots. Technical support is great and it’s been fun!

The post Interview: The Team Behind The Apple Harvesting Robot appeared first on Clearpath Robotics.

Transcript included.

In this episode, Per Sjöborg talks to Hans-Peter Grothaus, from CLAAS, about automation in agriculture.

There is a small window of time when crops are ready to harvest. If, during this window, not all of the crops are harvested, the farmer can experience large financial losses. This means that systems that automate fieldwork must be reliable. And reliability, or robustness, is a major challenge because field conditions change during the day (it could start raining) and it is tough to know soil conditions in advance. In this interview, these challenges are discussed, as well as how field data can help farmers make decisions and the market for agricultural robotics.

Hans-Peter Grothaus

Dr. Hans-Peter Grothaus studied Agriculture Sciences at the Universität Göttingen in Germany, where he wrote his dissertation. Since 2008, Grothaus has been the head of development for system-based services at CLAAS in Harsewinkel, Germany.

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Transcript

Per Sjöborg:  Welcome to the podcast. I’m here with Hans-Peter Grothaus from CLAAS, and we’re going to talk about automation and fleet integration within the agricultural community, a reasonably well established field in advanced automation. But it’s still growing and adding features as we go along. Could you tell our listeners, who may never have been on a farm, what kind of equipment you’re working with and how it is used by your customers?

Hans-Peter Grothaus:  What we’re using is harvesting machines and also machines for tractors, for overloading the harvested material, for infield processes and also for transportation, to bring the harvested goods to the farm.

Per Sjöborg:  Now we’re talking about corn, potatoes, and what else?

Hans-Peter Grothaus:  So, we work more with grain and grapeseed and all the fruits that are above the earth, nothing under the earth.

Per Sjöborg:  Yeah, because the harvester is in the middle of a network; it can’t do its job on its own; it has to have a fleet of tractors with carrying capacity and then trucks to get to a storage facility, and all of these integrate and work together. Could you tell us a bit more about the processes you use and how these are optimized?

Hans-Peter Grothaus:  So, traditionally we have single harvesting machines, with an overloading facility at the end of the field. But in bigger farms in larger surroundings, we also have multiple harvesting machines working together with overloading facilities, which bring the harvested goods to a truck waiting at the end of the field.

Per Sjöborg: So this means that there are actually two instances of offloading and unloading: from the harvester to the tractor, that is able to operate in the field, and then from the tractor to the truck that is only able  to operate on regular roads.

Hans-Peter Grothaus:  Yes, that’s true, because the trucks are normally not used in the fields, therefore we use tractors and special overloading facilities; overloading wagons which have low tire pressure so that the ground is not damaged by the tires.

Per Sjöborg:  And the vehicles don’t get stuck and so forth.

Hans-Peter Grothaus:  Yes.

Per Sjöborg:  And all this has to be optimized, of course, so that we use as little fuel as possible and so that we can harvest as much as possible in as short a time as possible. I guess that’s very hard to optimize properly.

Hans-Peter Grothaus:  Yes, you have different goals. For example, when you harvest depends on weather conditions and on the available equipment you have, and therefore you have to plan. We are developing infield planning systems. Just imagine you have a navigation system and you can say, “Okay, I want to be ready early with my harvest because bad weather is coming,” or you have very stable weather conditions and you say, “Okay, I have another target, I want to have the highest quality, with low fuel consumption,” or you want to have the same process but not drive over the whole field to avoid soil compaction.

Per Sjöborg:  Yes, because, as you’ve said, you have no roof over your operation and so you really feel the robotics operating out there under rapidly changing conditions. I mean, you can have a prediction of good weather but it can change immediately, or in a very short period of time, and you have to re-optimize everything.

Hans-Peter Grothaus:  Yes, you know that you have to harvest once a year, you know that weeks and years beforehand, but what you don’t know are the actual conditions. And you don’t know exactly what’s grown on a certain plot – that can vary a lot, even within a field. You don’t know the condition of the soil, how well you can drive on it or how weather conditions will develop throughout the day. So, when you start harvesting everything it could be optimal, and after two hours it’s not so optimal, so you have to change the settings of the fleet and the single machines.

Per Sjöborg:  Then, of course, this dynamically redefines itself all the time towards the overall goals of the operation.

Hans-Peter Grothaus:  Yes.

Per Sjöborg:  I understand that you use navigation systems, usually GPS. Do you also use any visual verification of where you are in the field, where other machines are in the field?

Hans-Peter Grothaus:  For steering purposes, we have different solutions on the market. We have camera based solutions and we have GPS based solutions, with correction signals that are accurate to two centimeters. So that works well. We also have laser-based systems that are for steering the machines. All these planning tools are in development. The first prototypes work quite well. The first step is to show the other drivers the actual location of a machine. When you imagine a field of wheat or barley, you can see the machine on the horizon, but when you have a maize field – maize is four meters high – you might only hear the machine, but not know exactly where it is. When you can see that the other machine is at a certain spot and you can see how full the overloading bin already is – this information helps you.

Per Sjöborg:  Especially, as you mentioned, in a maize field, where it might be very scary to see a big combine harvester only meters away!

Hans-Peter Grothaus:  Or you only hear it.

Per Sjöborg:  Yeah, but where is it? Because you’re in this high maize field, in a smaller vehicle.

Hans-Peter Grothaus:  Yes, but that is not the main danger, because you can react. For these inferior processes, you need the right overloading position. With a maize harvest, there is a lot of bulky material which you have to transport. Just for finding the right rendezvous point – where the transportation vehicles should meet, to avoid waiting time and additional driving – it helps a lot and takes the stress out of the process.

Per Sjöborg:  You use a lot of sensors to collect data about what is being harvested in real time. So, you know that, for instance, next year this area of the field can give X amount of return. What kind of sensors are you using to detect the quality of the grain etc.? They have to be very reliable and advanced, if the farmer only has a window of a couple of weeks or a month.

Hans-Peter Grothaus:  We use different cameras. For example, still cameras to detect grain quality, and this was hard to develop because there are difficult surroundings in which we use them and therefore they have to be very robust. They’re very reliable because, if they fail, then it takes a long time to fix. The farmer has to do his harvest right now, not in two weeks time right? Farmers want the machines to work without interruption during the harvesting period.

Per Sjöborg:  It’s a stressful period for the farmer.

Hans-Peter Grothaus:  Yes, so they have to be very reliable during the harvesting period. That could be part of the premium our customers pay for the products, because they’re very reliable. We do everything we can to ensure that the machine does not fail in the harvesting period. So there are lots of sensors and condition monitoring. We can make yield maps, so we can map exactly how much grain or other goods were harvested at a certain plot, and maps where you make overlays over a number of years and you can see how much you have harvested in a certain area. With this information, you can decide how much fertilizer to use on a plot to manage it. So, you see, there is an inter-linkage between the machines, processes over the years and the management systems. You have connected managing systems, with all the data from the whole farm and the machines input data but also use the data from this management system.

Per Sjöborg:  This also goes into the optimization process, of course. Say, if we have dry weather now but we are expecting wet weather, we can optimize to harvest the part of the field where the soil is usually the wettest, because it might be un-drivable if it rains further on in the day or the week. So you can optimize the data for that?

Hans-Peter Grothaus:  Yes, when you know that, you can start harvesting a certain area of a field earlier. You can also use your machine for a longer period, but when you are using a contractor for the first time, where should he get the information from, to know where to start? So, normally he drives around and then, after some time, he knows what to do, but you can save time if you give him additional information.

Per Sjöborg:  And in this reasonably small window of harvesting time, every hour, every half a day counts a lot. It’s very important to do it on time.

Hans-Peter Grothaus:  Yes, that’s true, and systems which help farmers to make better quality decisions are economical because an hour of harvesting time is very expensive, especially if you are not able to use it.

Per Sjöborg:  If weather catches up with you.

Hans-Peter Grothaus:  If the weather catches up and you have no additional capacity, that would be the worst case scenario.

Per Sjöborg:  Harvesting is one of your major businesses but you do other things in agriculture too. We’ve already mentioned pesticides and fertilizing, but there’s also tilling. Can you give us a short overview of your work in non-harvesting related activities?

Hans-Peter Grothaus:  We also have tractors and these act with implements on the farms, so we don’t have to produce sprayers or fertilizers. We produce machines for grass harvesting, for example. There is also a lot of intelligence, such as systems where the tractor is steered by the implement; the implement in these processes is much more intelligent than the tractor.

Per Sjöborg:  Because it knows what it’s doing and what it needs from the tractor to do it the best way.

Hans-Peter Grothaus:  Yes, the implement knows its own process, and the implement has more and more intelligence, and processes and computers. The idea is that the tractor gets an image of the implement and you can steer it, but the intelligence is in the implement.

Per Sjöborg:  Because it’s unique to a particular task. This is very interesting. As I understand it, the process of using automated and assisted driving is more or less standard today, so could you describe how these things have been selling in the farming industry, because I think that many people out there have ideas for robotics and need information on how to sell this to a community. How did you convince farmers that this reasonably advanced level of technology would work, and that it would be beneficial and they could handle it? How did you convince them that this was a good idea?

Hans-Peter Grothaus:  Most professional farmers and most customers can decide very rationally, if they see that a system helps them work for a longer period without exhaustion, and helps them to save money because the steering is more accurate. Just imagine you’re mowing your lawn and you always overlap a little, because you don’t want grass to be left. In the harvesting process, when you have to cut a bar with twelve meters, nobody is able to do it with such great accuracy and at such a distance, so we always overlap, maybe half a meter.

Per Sjöborg:  Which is a lot if you’re doing many fields.

Hans-Peter Grothaus:  Which is a lot if you’re doing kilometers of driving a day.

Per Sjöborg:  So if you can get that down to two decimeters…

Hans-Peter Grothaus:  With ten centimeters, you have gained a lot on fuel consumption. Less time means less soil compaction. So the steering systems pay off in one year now. Other systems? That is when our customers look at all the new features and say, “I don’t know if I need it.” But if they do buy it, they get used to it because the work gets easier; it’s more relaxing, less stressful and the farmer can, for example, do management tasks on the machine. You don’t need to do stupid work, like steering the machine. It does it on its own.

Per Sjöborg:  Yeah, and it does it better, but I guess he is better at the management though.

Hans-Peter Grothaus:  Yes, but he has to supervise the processes, to see if there are obstacles that the machine may not have seen.

Per Sjöborg:  This leads us to the future of these systems. Are we going to see these fleets become fully autonomous? You’ll have someone overseeing a fleet of these vehicles – a few harvesters and the truck distribution network we talked about – to the edge of the field. I don’t think that the actual truck taking it to the storage facility is a problem. Will we see fully automated fields in the near future?

Hans-Peter Grothaus:  I can imagine that we’ll see more “master-slave” based systems in the near future. The labor cost is not the limiting factor at the moment, because the machines are not very cheap but, on the other hand, you have to bring all the machines into the field and you have to get them back. Therefore you have to change the logistics when you have several machines with only one driver. So you have new challenges. There are ideas in academia about swarm robots, which could harvest. But at the moment, especially in the harvesting process, we have lots of very messy goods. We have to transport many tons and that is not suited to tiny robots, so they should be big robots.

Per Sjöborg:  Then they become dangerous robots.

Hans-Peter Grothaus:  They will become more dangerous for man and then you have new challenges.

Per Sjöborg:  As you’ve pointed out, even if they were autonomous in the field, you’d still have to bring them into the field.

Hans-Peter Grothaus:  You have to bring them and maintain them – you have to recharge them when they’re electrically powered. So, if you’re monitoring; if you have a small robot which monitors the health of plants, for example, that could drive around and give us this information.

Per Sjöborg:  “Do I need to add more fertilizer or water or pesticides?” It could do those kind of things.

Hans-Peter Grothaus:  Yes it could also do fertilizer and pesticides. But, as a first step, it would just give information.

Per Sjöborg:  Yes, to determine if we need to.

Hans-Peter Grothaus:  If we need to, and then give this information to the process.

Per Sjöborg:  So, for instance, it could go over a field and detect where there are a lot of weeds, to see if more pesticides are needed or if the grain isn’t developing properly and needs more fertilizer. It could also give you advanced information about where the crop hasn’t grown.

Hans-Peter Grothaus:  Yes, and bear in mind that these vehicles would travel very slowly, and they have a lot of time. They’d make maps of the field. Then, the sprayer or the fertilizer machine, with bags of material or fertilizer, would go in at high speed, and it would have an exact map of where to spray.

Per Sjöborg:  That would take the optimization we talked about in the beginning a step further; it wouldn’t only rely on the data you had last year, it would actually look at the field and base the optimization on how it appears now.

Hans-Peter Grothaus:  There are different systems just to decouple the processes.

Per Sjöborg:  Very interesting. So, where do you see the future? What are you working on in your secret labs right now? Where is the cutting edge of infield agricultural operation?

Hans-Peter Grothaus:  The first step is more autonomy in the infield process, in the logistics. We have installed high capacity in the machines but sometimes, in the logistic processes, there is a reason for the gaps so the farmer does not get the installed capacity. Aside from the infield logistics, you also have outfield logistics. So, where there are no roads, the normal navigation system doesn’t always have the right information for our surroundings.

Per Sjöborg:  No, it’s not on a paved road, you’re out there in the field and the maps aren’t very accurate.

Hans-Peter Grothaus:  Yes, and it doesn’t know where the right spot is and where there are obstacles like electricity wires, telephone cables and trees. This information is not collected today and it could help to make it available, to make plans for the outfield logistics.

Per Sjöborg:  Yes, and entrance and exit points for a field. The system could also tell you that a point is placed incorrectly and we could optimize much better.

Hans-Peter Grothaus:  Yes.

Per Sjöborg:  Then you could take the information you gathered and optimize and even adapt to that. Yeah, very interesting.

Hans-Peter Grothaus:  You can also simulate processes and you can use your simulation to educate the driver. Because there are only six to eight weeks in the harvesting period, and, for the rest of the year, the farmers aren’t harvesting and the drivers are not trained, so they need a couple of days to get into the process again.

Per Sjöborg:  They’re better at the end of the harvest than they are in the beginning. It’s like going on a skiing holiday, when it was a couple of years ago that you last skied, and you’re a much better skier the last day of your holiday than the first. But if we could train someone properly ahead of time, we could optimize this critical window of opportunity we have at harvesting time.

Hans-Peter Grothaus:  We have a certain period where we can do the work but we could use historical data and simulation to train the drivers.

Per Sjöborg:  Perfect. Thank you very much for taking the time to do the interview.

Hans-Peter Grothaus:  Thank you, it was a pleasure for me.

All audio interviews are transcribed and edited for clarity with great care, however, we cannot assume responsibility for their accuracy.

By Rachel Gould

The 2016 agBOT Robotic Seeding Challenge challenges participants to build unmanned robotic equipment to plant, measure and track multiple crop seeds to improve farming efficiency. The objective of the agBOT challenge is to reduce the harmful chemical by-products and erosion caused by inefficient farming techniques. The challenge will also inspire new solutions to reduce farms’ carbon footprints while increasing production – essential given that the agricultural sector must support the projected global population of nine billion by the year 2050. Clearpath Robotics, in conjunction with airBridge, is proud to offer a $50,000 grant toward the purchase of the Grizzly Robotic Utility Vehicle for teams in the 2016 challenge.

More about agBOT

The 2016 agBOT competition will be held on May 7, 2016. Additional challenges are set for 2017 and 2018. The 2016 competition will be hosted by Gerrish Farms in Rockville, Indiana, where competitors will be challenged to revolutionize the industry by improving precision and efficiency.

Feeding the world’s nine billion people

Although farming has become mechanized, the evolution of agricultural techniques to include unmanned robots provides a unique opportunity. The agricultural sector can increase productivity and sustainability by using intelligent robotics to analyze current farming practices including fertilization and seedling variety. This concept is paramount in the world of shrinking farming circles and an ever-growing population.

The bot to get it done!

Grizzly RUV seeding a field.

This year’s agBOT competition requires a robot that can function as an unmanned crop seeder; it must plant two types of seeds over half-mile-long rows. It must also supply real-time data using a mobile tracking antenna and a variety of analytics including down pressure and variety placement. Participating teams are responsible for developing all software, sensors and human-machine control interfaces to control tasks.

This complex list of requirements requires a flexible, rugged, high performing solution, which is why we’re excited to partner with airBridge to offer a $50,000 grant for Grizzlies that are used in the competition.

Grizzly RUV in a corn field.

Grizzly is Clearpath’s largest all-terrain battery operated robot. The mobile research platform offers the performance of a mini-tractor and the precision of a robot with a max payload of 1320 lbs, max speed of 12 mph, 8 inches of ground clearance, and 5V, 12V, 24V and 48V user powers. See here for all technical specs.

Ready to participate in the agBOT 2016 challenge? Want to take advantage of this unique Grizzly grant opportunity? Get in touch with one of our unmanned experts.

• Drought and desertification: How robots might help
• 2 agricultural robotics projects funded under latest Horizon 2020
• Are agricultural robots ready? 27 companies profiled
• Will agricultural robots arrive in time to keep fruit and vegetable costs down?

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Dry California riverbed. Source: Wikimedia Commons

Groundwater levels in California’s Central Valley are down to historic lows and reservoirs have been depleted following four consecutive years of severe drought in the state. California is set to introduce water rationing in the coming weeks, and though the new rationing rules will focus on urban areas and not farms for the time being, they serve as a warning bell to farmers who will inevitably need to adapt to the effects of climate change on food production. Long term solutions are needed to help make agriculture drought resistant. How could robotics help?

A NYTimes article published April 2nd, Mapping the Spread of Drought Across the U.S., leads off with an animated map supplied by the National Drought Mitigation Center, which shows the spread of drought conditions across the contiguous 48 states since late fall, 2014.

From that article:

Droughts appear to be intensifying over much of the West and Southwest as a result of global warming. Over the past decade, droughts in some regions have rivaled the epic dry spells of the 1930s and 1950s. About 37 percent of the contiguous United States was in at least a moderate drought as of March 31, 2015.

There are two major ways in which robots might help with the effects of climate change upon food production.

Most immediately, robots might operate indoor production facilities to produce high value, quickly maturing crops requiring moist environments. Because space would be at a premium in such facilities, and in most cases the lighting would be artificial, to make most best use of both, plants would need to be gradually spread apart as they grow, an operation well suited to pick-and-place robots, assisted by mobile robots serving them trays of plants. To operate most efficiently, that artificial light should be predominantly red and blue, since green light is mostly reflected away by plants; this might prove a stressful environment for human workers, but robots won’t care. Moreover, once an optimal configuration has been determined – how many trays of plants one pick-and-place robot can keep up with – the entire operation could easily be replicated as many times as needed, constrained only be available space, electrical power, and a market for the produce.

The other way in which robots might help is in dry fields under the hot sun. This could be as simple as reflective umbrellas, nets, or horizontal shutters that shade the ground from the mid-day sun, but uncover it again in the late afternoon to allow built up heat to radiate into the night sky. Robots could also maintain drip-irrigation systems or make daily rounds to inject water into the soil near root crowns.

image from: United States Drought Monitor

In principle, they could also perform planting, weeding, pest control, pruning, harvesting, and deal with plant materials left behind after harvest, and do it all while working a mixture of annuals between and around standing perennials – although much of the technology needed for such a scenario remains to be developed.

On the other hand, given that level of utility, much could become possible that currently is not. The weight of heavy machinery could be kept off of productive soil, rendering it more capable of holding water. Mulch could be applied at any time. When expected precipitation fails to materialize, plants could be pruned to reduce their leaf area and the amount of water they require. Windbreaks could be installed surrounding relatively small patches of land, in a manner not conducive to tractors and conventional implements, but affording much better protection from drying winds as well as providing a secondary crop of woody fiber and habitat for wildlife. If planted in low berms, those windbreaks could also help to keep what moisture there is in the fields and eliminate water erosion.

The benefits of such technology aren’t limited to coping with drought, of course, but given that drought is likely to be a widespread, persistent problem, robotics could be used to help keep marginal land – which might otherwise turn to desert – in sustainable production, and perhaps even help to reclaim some land that has already been lost to desertification, beginning, for example, with the construction of windbreak fences (like snow fences) to accumulate wind-blown dust that will become the berms into which living windbreaks can be planted. Windbreaks are an essential permanent feature of productive land in arid and semi-arid environments, so if a robot or set of robots is to tend land that is affected by drought it/they should be able to perform the operations necessary to establish and maintain a windbreak.  Many windbreaks have been torn out because they impede the movement of large tractors, which require large fields to operate efficiently. Removing the motivation to tear out windbreaks and bringing the capability to replant them where they’ve already been torn out is one major way robots could contribute to making agriculture drought resistant.

Agriculture is one of our most important industries. It provides food, feed and fuel necessary for our survival. With the global population expected to reach 9 billion by 2050, agricultural production must double to meet the demand. And because of limited arable land, productivity must increase 25% to help meet that goal.

Consider these factoids:

• Major US farming conglomerates are buying foreign land and beginning to farm there, citing lower overall cost.
• China is buying land in Africa and sending skilled workers to supervise those new farms.
• Farmers and ranchers the world over are transitioning to precision agricultural methods, i.e., subdividing their acreage into many sub-plots, in some cases, right down to the individual plant/tree/animal thereby enabling increased productivity and lower overall costs.
• Unmanned aerial vehicles are being used to map, observe, sense and spray.
• Unmanned (or at least autonomous) ground vehicles are providing more precise movements and thereby enabling precision practices.
• The US Bureau of Labor Statistics reports that 2012 median pay for farm workers was $9.09.
• The US Bureau of Labor Statistics reports that there were 749,400 ag workers in 2012, down 3% (25,000) from 2011.
• 74% – approximate number of crop workers in the US who were born in Mexico or Central America, of which more than half are likely to be undocumented (according to Fortune Magazine).
• Cropdusters have the 3rd highest fatality rate among professions in the US. 90% of crop spraying in Japan is done using small unmanned helicopters.

Thus the agricultural industry is in transition. And that transition differs country by country, state by state, region by region as well as by type of farming practiced: from primitive to conventional to precision to experimental. A little bit of everything is going on everywhere but the general trend worldwide is toward precision agriculture supplemented by advanced technologies, including robotics.

Many factors are precipitating these changes in addition to global population growth and the cost and availability of labor: the diminishing availability and increasing cost of water, political and regulatory procedures and hold-ups; limited tillable acreage; better, cheaper and faster technological automation products; and climate change, to name just a few.

Modern farmers and ranchers are already high-tech. Digitally-controlled farm implements are regularly in use. There are partially and fully automatic devices for most aspects of agricultural functions from grafting to planting, from harvesting to sorting, packaging and boxing. Farmers use software systems and aerial survey maps and data to guide their field operations. They also use auto-steer systems included in many new tractors (or buy kits that do the same thing) that follow GPS and software guidance. Some farmers are already transitioning some of their operations to full autonomy. Thus forward-thinking farm owners today may be able to skip over slow, incremental improvements and jump directly to robotic and autonomous automation. But are the robots ready?

In a follow-up to my July, 2014 post “Will agricultural robots arrive in time to keep fruit and vegetable costs down?”, this article profiles 27 of the many companies (from conglomerates to start-ups) attempting to provide robotic solutions for farming problems and explores what they are doing, when their products will be available, and at what cost.

[NOTE: From a list of 60 organizations involved with agricultural robotics, dairy was eliminated even though robotic milking systems are amazing and a growing business. Also eliminated were companies that didn’t (or couldn’t because they are publicly traded such as John Deere and CNH Industrial (Case/New Holland)) respond to my brief questionnaire. Further, only one of the many university ag research facilities was profiled even though there’s a lot of rich science under development; I wanted to focus on the here and now; not the future.]

Companies Mentioned (sorted by primary function):

Harvesting and Tractors – Tractors provide two things: guidance to the devices they are towing, and pulling power. Current tractors are huge, and if they break down, the entire operation comes to a halt. Autonomous machines don’t need operators and can operate around the clock. Thus tight operational windows can be achieved for seeding and other time-sensitive activities.

• Agrobot
• Energid
• Clearpath Robotics
• Autonomous Solutions
• Wageningen UR
• Agritronics
• Kinze Manufacturing
• Amazone-Bosch
• AGCO Fendt
• Rowbot
• Robotic Harvesting

Planting, Pruning, Potting, Grafting and Nursery Operations

• Harvest Automation
• ISO Group
• Helper Robotech
• Conic Systems
• Wall-Ye

Thinning and Weeding

• Blue River Technologies
• ecoRobotix
• Vision Robotics
• F Poulsen Engineering

UAS, Inspection, Data Collection and Data Manipulation – UAVs are only as good as the other precision ag equipment and systems; if there are no computers on the tractors or controllers on the implements towed, and if they can’t talk to each other, UAS data they collect are just pretty pictures.

• Agribotix
• PrecisionHawk
• senseFly

Smart Implements – New implements incorporate advanced control systems and can respond to commands from the towing tractor or provide their own mobility and navigation.

• Jaybridge Robotics
• Autonomous Tractor
• Agrobotics AutoProbe
• Naio Technologies

Agriculture is big business in every country around the world. Thus this is a timely review of the progress to bring robotic automation to an already automated industry. Bottom line: lots of activity, much of which will be coming online in the next year or two, but little market penetration thus far.

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Company Profiles:

Company: ISO Group, a Flier Systems company, Gameren, The Netherlands
Website: www.isogroepmachinebouw.nl
Product: RoBoPlant flower planting robot and fully and semi-autonomous grafting robots

ISO Group’s flower planting robot RoBoPlant

Area of use: All over the EU
Function: Semi and full automatic machinery for greenhouse or protected horticulture. Flower planting robotic system takes flats of peat seedlings, separates them and plants them in chosen patterns;
Testing: Continual testing and development
Availability: Began selling products in 2002
Price: N/A

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Company: Autonomous Tractor, Fargo, ND
Website: http://www.autonomoustractor.com/
Product: Modular cab-less robotic tractor implement platform

Autonomous Implement – Spirit Mower

Area of use: Hay producers in the US
Function: Mowing hay. Can add modular engine power as needed by the type and size of implement. Will expand to other crops and other non-ag industries after mower begins shipping
Testing: Continuously field testing
Availability: Expect to come to market and begin shipping pre-orders in 2016
Price: Price expected to be 1/2 of similarly-powered tractors and implements

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Company: Blue River Technologies, Sunnyvale, CA
Website: http://www.bluerivert.com/
Product: LettuceBot2 (2nd generation) lettuce thinning

Blue River Technology 3-row LettuceBot2

Area of use: CA and AZ lettuce fields (covers 80% of US lettuce production)
Function: Thinning and weed spraying of iceberg, romaine and leaf lettuce
Testing: Recently completed field testing 3rd generation machine; selective weeding used to improve germination
Availability: Began operating as a per-acre service in 2013
Price: Price per acre depends on the lettuce planting configuration but equates to a slight premium over manual labor costs

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Company: Agrobot, Huelva, Spain
Website: http://www.agrobot.com/
Product: Agrobot SW6010 and AGSHydro, a bed-on hydroponic growing system customized for strawberry growing and harvesting

Agrobot harvester

Agrobot hydroponic growing system

Area of use: Strawberry harvesting in Oxnard, CA
Function: Ripe berry picking from raised hydroponic growing beds
Testing: Will start final testing strawberry harvesting in January; have done seasonal testing for a few years
Availability: Mid-2015
Price: $250,000 for a harvester with 60 robotic picking arms. Says one large berry farmer: “The Agrobot works on several investment paths.  One where we harvest cheaper than we do today and another where we harvest fruit that there are not sufficient people for.  In the latter case the Agrobot pays off instantly because without the ability to harvest we do not have a business (this is becoming more common).”

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Company: Agribotix, Boulder, Colorado
Website: http://agribotix.com/
Product: Drone services for precision agriculture

Agribotix Hornet Drone

Agribotix image processing services

Area of use: US Midwest (CO, KA, MO, etc.)
Function: Lease ag drones to co-ops, agronomists, crop consults, farm managers and big industrial farm corporations; produce and process hi-res images and maps using various sensors, and provide prescription maps to match the application of fertilizer to the places that need more (or less)
Testing: Ongoing testing with pilot customers
Availability: Began selling services in 2014
Price: About $8,000 for a season which includes training, drone use, stitched-together RGB and infrared images, crop health and prescription maps. Agribotix offers an image processing services contract with per acre charges for various maps and images over an annual contract period

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Company: Wall-Ye, Macon, France
Website: http://wall-ye.com
Product: Wall-Ye 1000 mobile pruning robot

Wall-Ye 1000 Pruning Robot

Area of use: French grape vineyards pruning
Function: Autonomous pruning
Testing: Completed in 2013
Availability: For sale and as a service
Price: $30,000 per robot

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Company: ecoRobotix, Essert-Pittet, Switzerland
Website: http://www.ecorobotix.com/
Product: Lightweight autonomous robots initially for weeding

ecoRobotix concept field robot

Area of use: Field testing in Switzerland; next year in Germany
Function: A robotic platform for weeding of spaced-row cultures, which includes advanced weed recognition algorithms, fast robotic arms, advanced sensor technology, high energy efficiency, and wireless communications
Testing: Currently with sugarbeet but plan to extend to colza, sunflower, corn and soya
Availability: First machines available for sale by end of 2015
Price: About 15’000 EUR ($18,750) per robot

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Company: Energid, Cambridge, MA
Website: http://www.energid.com/experience/citrus-harvesting/
Product: Citrus harvester

Energid towed multi-arm citrus harvester

Area of use: Florida citrus orchards; oranges (early and late season) and grapefruit
Function: Harvesting, initially for juice
Testing: Will test again during seasons in Florida in 2015 and 2016
Availability: Expect to have initial product in late 2016
Price: System to cost $300,000-$400,000

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Company: Harvest Automation, North Billerica, MA
Website: http://www.harvestai.com/products
Product: HV-100 mobile robot

Harvest Automation HV-100 Mobile Robot

Harvest Automation potted plant movement schema

Area of use: Nurseries (ornamental, berries, tomatoes, etc.)
Function: Material handling, movement of containers, spacing.
Testing: HV-100 testing completed
Availability: Been selling since 2013
Price: $130,000 for a team of four robots to purchase.  We also rent teams of four for $30K/3 months.  The rental scheme has worked really well.  All who have rented then subsequently purchased.

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Company: Clearpath Robotics, Kitchener, ON, Canada
Website: http://www.clearpathrobotics.com/grizzly/
Product: Grizzly RUV (cab-less robotic utility vehicle) and Husky UGV

Clearpath Grizzly RUV pulling implement

Area of use: Sold to university research facilities for ag applications development
Function: Harvesting, mowing, hauling, research
Testing: Testing asparagus farming with added laser scanner to identify appropriate stalks and a cutter inserted into the soil to cut the stalk below ground; hauling manure from chicken farms while cleaning out barns; detecting where cows urinate and then treating the area so grass can continue to grow; mowing inbetween orchard rows and hauling (hay/straw wagons back to barn and return so that the farmer doesn’t have to stop baling; hauling chemical refills to sprayer locations; hauling manure spreader)
Availability: Early 2015 – at present only selling to academia and research organizations
Price: $12,000 to $100,000 depending on configuration

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Company: Autonomous Solutions, Petersboro, UT
Website: http://www.asirobots.com/farming/
Product: Forge Robotic Platform, a kit for enabling a skid steer to operate autonomously or remotely controlled

ASI skid steer with cab in vineyard

ASI skid steer cab options

ASI Universal Vehicle Automation Kit

Area of use: Wine vineyards
Function: Mowing and spraying functions
Testing: Running field trials in CA and TX
Availability: Mid-2015
Price: $75,000 – $150,000/unit (includes complete skid steer device and driving kit) depending on skid steer configuration

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Company: Wageningen UR (University and Research center), Wageningen, The Netherlands and Agritronics, Sint Annaparochie, The Netherlands
Website: http://www.wageningenur.nl/en/Expertise-Services/Research-Institutes/Wageningen-UR-Greenhouse-Horticulture/Research-themes/Advanced-Cultivation-and-Production-Systems/Subthemes/Computer-vision-and-robotics.htm and http://www.agritronics.nl/
Product: Research to supply intelligent systems for high value crops to commercial research partners/vendors

Wageningen UR cucumber harvesting robot

Area of use: Sweet peppers in The Netherlands, apples and grapes in Belgium, canopy spraying in Slovenia and spot spraying in Italy
Function: Harvesting and spraying (spot and canopy)
Testing: Yes, for sweet pepper, in July in a commercial greenhouse; for apples and grapes tests are now completed. For spraying, field tests have been completed. A new harvester, visual quality inspection and vision system for broccoli, is being developed with start-up Agritronics, Sint Annaparochie, The Netherlands
Availability: “This will take several years”
Price: Not available

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Company: Vision Robotics, San Diego, CA
Website: http://www.visionrobotics.com/
Product: Lettuce Thinner and Grape Vineyard Pruner

Vision Robotics 6-Line Lettuce Thinner

Vision Robotics grapevine pruner

Area of use: California
Function:  Grapevine pruning being tested; lettuce thinner available for sale
Testing: Testing and development for grapevine pruner could be completed in less than 18 months depending on funding
Availability: Lettuce thinner available now; pruner early 2016
Price: Lettuce thinner starts at $140,000 and upwards depending on configuration; pruner will likely sell for same amount

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Company: Precision Hawk, Raleigh, NC
Website: http://precisionhawk.com/
Product: Lancaster UAV with various plug and play sensor options plus Datamapper

PrecisionHawk Lancaster

PrecisionHawk Lancaster plug-in sensors

Area of use: Ontario, Canada
Function: Providing data for crop researchers, consultants and farmers and ranchers to make farm management decisions
Testing: Performing field tests under an SFOC from Transport Canada for a number of years. The majority of research and development happens in Ontario, Canada. Over the past six months have obtained a number of CoAs from the FAA to perform field tests and research across the United States in conjunction with universities such as NC State, Texas A&M, Kansas State and Cornell.
Availability: 70% of sales are global. Have entered into a number of projects with US companies on foreign soil for specific research projects
Price: Basic Lancaster platform is $15,000 plus sensors and other options

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Company: F Poulsen Engineering ApS, Hvalso, Denmark
Website: http://www.visionweeding.com
Product: ROBOVATOR thermal and/or hydraulic weeder

Poulsen weeder

Area of use: 30 machines already at work in UK, the EU and Canada.
Function: Weeding and thinning of lettuce, cabbage, fennel and onions
Testing: In California (preceding expansion into North America)
Availability: Started selling in 2011 after 8 years of development
Price: The 5-row version sells in Europe for 80.000€ ($100,000)

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Company: Kinze Manufacturing, Williamsburg, Iowa and Jaybridge Robotics, Cambridge, MA
Website: http://www.kinze.com/ and http://www.jaybridge.com/
Product: Autonomous vehicle system for row crop harvesting

Kinze autonomous tractor and grain cart

Area of use: Iowa and Illinois corn and soybeans
Function: Autonomously garner row crop grains from combine machines and bring it out of the field to the transport area
Testing: Testing autonomous harvesting system since 2012; in 2013 three farmers in Iowa and Illinois leased systems without Kinze overseeing operation allowing the farmers to use the technology independently. The Kinze system marries off-the-shelf components, including GPS, radar, laser sensors and video cameras, with custom software that allows the system to react to field obstructions. It was developed in partnership with Jaybridge Robotics.
Availability: Kinze is not currently selling the harvesting system but is working towards full commercialization soon
Price: The price has not yet been set for the system which includes the autonomous driving kit for the tractor and grain cart plus the navigation, path planning, harvester communication and control software systems

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Company: Agrobotics, Little Rock, AR
Website: http://www.agrobotics.com/
Product: AutoProbe soil sampling system

Agrobotics AutoProbe

Area of use: Midwest US farm belt
Function: AutoProbe is a towed device which directs the driving of the towed vehicle to enable consistent, uniform and accurately distanced soil samples. The device is capable of pulling over 2,500 cores per hour
Testing: Tested for 7 years in the Mississippi Delta in the Midwest US
Availability: Available now as both a service and a sale; live demos at various US ag shows
Price: Not available

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Company: Amazone-Werke Gmbh, Hasbergen, Germany
Website: http://info.amazone.de/DisplayInfo.aspx?id=14033
Product: BoniRob field robot

Amazone-Bosch BoniRob lightweight field robot

Area of use: Work on corn and wheat experimental plots in Germany
Function: Autonomous omnidirectional field robots working in “flocks” for multiple purposes
Testing: Multiple-purpose lightweight robot for weeding, applying fertilizer, inspection being developed with Robert Bosch GmbH
Availability: Only two built; no plans announced for commercialization at this time
Price: No information available

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Company: Helper Robotech, Gimhae City, Korea
Website: http://helpersys.co.kr/
Product: BoniRob field robot

Helper Robotech fruit and vegetable grafting robot

Area of use: Korea, Japan and China
Function: Grafting is most common in European and Asian countries as well as in greenhouses worldwide where crop rotation is no longer an option and available land is under intense use. Robotic grafting is relatively new although mechanically-assisted grafting has been going on for a long time.
Testing: Unknown
Availability: Available now
Price: Unknown

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Company: AGCO Fendt, Deluth, GA
Website: http://www.agcocorp.com/GuideConnect.aspx
Product: GuideConnect, SectionControl and VarioGuide

AGCO Fendt GuideConnect – driverless 2nd system

AGCO Fendt VarioGuide auto steering system

Area of use: Global
Function: SectionControl integrates various data and enables fully automatic section control via GNSS for ISOBUS-capable sprayers, spreaders and seeders; the VarioGuide night and day auto steering system; and the new GuideConnect in which two tractors act as a unit where one vehicle is unmanned
Testing: GuideConnect is still under development with no known date or area for availability; the following vehicle doesn’t have its own obstacle detection which may be why they haven’t yet released the product
Availability: All but GuideConnect are available now in the EU and US
Price: Not available for all 3 systems

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Company: Rowbot, Minneapolis, MN
Website: http://rowbot.com
Product: Rowbot is a self-driving, multi-use platform that travels between rows of corn, ex: applying nitrogen fertilizer in sync with corn needs. It can also collect sensor data to inform both current and future work. GPS and several sensors keep the robot from trampling the crop

Rowbot in cornfield. Rowbots work in teams to apply nitrogen fertilizer in sync with precision needs

Area of use: US Corn Belt
Function: Rowbot travels between corn rows – often under the leaf canopy – to apply nitrogen fertilizer and also to seed cover crops
Testing: Working in conjunction with Carnegie Robotics on development of the Rowbot.
Availability: Began test marketing this year for in-season nitrogen and cover crop seeding services; plan to widen scope of services in 2015
Price: No information available about the cost of the service

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Company: senseFly, Cheseaux-Lausanne, Switzerland
Website: http://www.sensefly.com
Product: eBee Ag

senseFly eBee Ag

senseFly eBee Ag autopilot system and carrying case

Area of use: Global
Function: The eBee ag system includes eMotion software and a carrying case. The software and cameras enable 2 cm per pixel resolution and produce 3D maps and overlays as well as the capability to lay out (and simulate) a flight path for up to 45 minutes of flying time
Testing: 
Availability: The eBee ag system is available now
Price: About $12,000 for the complete system

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Company: Conic Systems, Barcelona, Spain
Website: http://www.conic-system.com/
Product: EMP-300 Grafting Robot

Conic Systems EMP-300 Grafting Robot

Area of use: Global
Function: Enables grafting of vegetables  and other greenhouse plants
Testing: Unknown
Availability: Now
Price: Not available

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Company: Naio Technologies, Toulouse, France
Website: http://naio-technologies.com/
Product: Naio Technologies Oz field robot

Naio Technologies Oz field robot

Area of use: Mostly in France
Testing: Testing next generation of Oz robot (with improved navigation capabilities) in real field conditions in France
Function: The Oz robot serves as an autonomous electric tractor which can be used for weeding and as a transport from harvesters to accumulation points. Oz operates as a self-powered robotic implement rather than a towed implement
Availability: Began selling in 2013
Price: Initially robots are being rented to help customers get familiarized with the product line and to help optimize the utilization. Units are renting/leasing for $315 to $475 per month depending on configuration

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Company: Robotic Harvesting, Simi Valley, CA
Website: http://www.roboticharvesting.com
Product: Strawberry harvester, data collector and mobile platform

Robotic Harvesting Strawberry Harvester

Area of use: California
Testing: Ongoing in California
Function: Autonomous mobile device which takes stereovision photos to locate any fruit or vegetable in 3D space and then uses a robot arm to pick and place on a conveyor selected berries
Availability: Unknown
Price: Unknown

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If you liked this article, you may also be interested in:

• Ten lessons for farm drones
• Matching technology to value creation: Drones in agriculture
• Will agricultural robots arrive in time to keep fruit and vegetable costs down?
• Robots Podcast: Blue River Technology
• Harvey: A working robot for container crops

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Link to audio file (27:16)

In this episode, Ron Vanderkley speaks with David Dorhout from Iowa State University about his Agricultural Robots that include Prospero the robot farmer and Aquarius the greenhouse watering robot.

Today’s agricultural equipment has been designed around a single farmer controlling large machinery. This method has its drawbacks since farming decisions have to be made at the level of the field. Nature instead is chaotic and dynamic, soil nutrients and moisture change from foot to foot. A swarm of small robots like Prospero would have the ability to farm inch by inch, examining the soil before planting each seed and choosing the best variety for that spot. Ideally, this would maximize the productivity of each acre, allow less land to be converted to farm land, and ultimately feed more people.

Prospero is the working prototype of an Autonomous Micro Planter (AMP) that uses a combination of swarm and game theory to plant seeds at safe distances from one another.

Dorhout’s second agricultural robot Aquarius is a greenhouse robot that autonomously waters plants using its 30 gallon tank. The robot is programed using taping on the ground of the greenhouse.

David Dorhout
David Dorhout is a graduate of Iowa State University. He has always been interested in robotics and has 14 years of experience in the agriculture and biotech industry doing field and greenhouse discovery work. He is the founder of Dorhout R&D LLC which is a research and development business designing and building novel robotic systems and interactive consumer electronic devices.

Links:

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• David Dorhout’s Website

You probably have a pretty good sense that I am a firm believer that precision agriculture and information is a big part of the answer. This is all about leveraging technology to provide more timely and accurate data in a way to increase efficiency and productivity by cutting time and overall cost. It is about doing more with less. But how are we getting there?

One difficulty with wide adoption of UAVs, in general, is that companies develop and sell them with the mentality, ‘Here is a great solution… Lets go find a problem it can solve.’ As UAV companies are quickly discovering, the key to making a successful platform is to make it as hands-off, easy to use and as industry specific as possible. In agriculture, farmers are not interested in learning how to be, or dedicating somebody to be, a UAV operator. They need a tool that is a part of their everyday workflow.

With that approach in mind, our team at PrecisionHawk did not actually start out to build UAV platforms. From the beginning, it was apparent to us that the agricultural market was not well served from a data perspective, and we had a clear focus as we began building the internal intelligence. The more users we spoke to, the more obvious it became. If you could survey land with an extremely tall stepladder, people would do that! In other words, all that matters is the data. So, the frequency with which it can be collected, its quality and its ability to be processed, managed and stored becomes critical to how useful it is.

You may have an advanced, smart UAV, but where does the value of that system lie? At PrecisionHawk we have been fully invested in the agricultural industry, and as we approach the new year, we’ve identified three areas of emphasis necessary to advancing precision agriculture.

1. The Sensors
Provide more options. Basic visual imagery can be extremely useful, but how can you get the most out of your aerial surveys? Companies are already beginning to understand the power of hyperspectral and lidar sensors and how they provide more complete and meaningful sets of agricultural data. Hyperspectral sensors, like the one that we fly on our Lancaster platform, show the user 100 channels of light, identifying a big picture that is invisible to the human eye. These sensors also provide consistent 1 cm resolution, which is a game changer for improving yield. Lidar sensors perform short range, high resolution, topographic analysis specifically where vegetation exists and when photogrammetry is unable to provide the required information. These tools bring a new dimension to analyzing data in problem areas, and these are just a few examples of sensors that we use. The available payloads run from UV, through visual, near IR and into thermal IR can be just as critical for obtaining data. When you look at the data pipeline that will allow growers to make informed decisions about their crops, you need to have the right sensor at the right time. That means options.

Make your platform flexible. I anticipate a big move towards payload systems that are swappable and removable. It always comes back to ease of use. A farmer will be able to collect more of what they need when they need it if sensors and batteries have the ability to be changed quickly. I strongly believe in this proposition, and these principles have helped guide us as we move towards opening our own payload system.

2. Crop Scouting Technology
Move data quickly through the cloud. Surveyors need access to usable data immediately. Moving a lot of data from a UAV into the cloud and down into a user’s hands can take a lot of time. The future is a farmer who is able to see the instant result of his flight. While some aspects of the data do require time to process and analyze to become useful information, there is also huge value in being able to see survey results immediately. Oftentimes, being able to look at a comprehensive presentation of the data allows a scout or grower to go and ground-truth the problem without ever having to schedule a second trip to the field.

Go mobile. Mobile is king. Every other business puts a premium on their mobile presence, and agriculture should not be excluded. Farmers will soon be able view, manage and share processed data, instantly from the field, on their smartphone or tablet.

3. Scalable Data
Moving big data in the UAV space. Again, it all comes back to the data. When UAVs are taking 900 plus snapshots of visual imagery in a day, where does that data go and how do you manage it? We’re talking about terabytes of information. Finding the quickest and most cost effective ways to automatically process data, store it securely and manage it for ease of use and scalability will be necessary.

You have to automate. Automation results in rapid turnaround time and the ability to provide competitive pricing. When data is collected with a high level of confidence, it should have the capability to be automatically processed without a human touch point. Companies that don’t adapt to this model will most certainly be left behind. The key feature in this model is that systems must be able to guarantee that the information collected will be useful, high quality data. This is an industry that is all about short time windows for understanding what is happening and taking action. Often there isn’t an opportunity to go back out and get the data again.

With all of that said, and rightly so, about data, we still need to go back to the plane, as the collection mechanism is critical to the quality of the information you receive. We continue to make great strides through R&D with a focus on enhancing the user experience and delivering meaningful results. By integrating more ‘smarts’ onto the plane, we’re able to make our platform easier to use and less prone to human error. Each will be key in helping farmers drive decisions that will increase productivity and efficiency and, most importantly, overall yield.

http://www.youtube.com/watch?v=4J0Rf-LFvYY

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This article outlines some of the key technologies that enable autonomous farming, using the Kinze Autonomous Grain Harvesting System as a case study.

Jaybridge Robotics automates vehicles for driverless operation in industrial domains including agriculture and mining. We do this using commercial, off-the-shelf (COTS) components and our software. We believe software is the greatest challenge in making vehicular robotics cost-effective and reliable. For the last few years, we have been working with Kinze Manufacturing to automate their line of agricultural equipment (see video above). In this article, I’m going to explain why farmers are technologically well-positioned to take advantage of automated farm vehicles. I’ll also provide a case study of the technology behind the Kinze Autonomous Grain Harvesting System

Satellite-guided farming

Although urbanites may still think of farming as low-tech, backward profession, a great deal of professional farming has gone high-tech in the US and other developed nations. The last decade, in particular, has seen the rapid embrace of high-tech under the general label of Precision Agriculture.
Farmers collect and act on copious amounts of data. Global Positioning System (GPS) data from satellites (see Figure 1) lies at the heart of Precision Agriculture. Farmers use farm management software (e.g FarmWorks) and GPS receivers to map their fields, and to track the yield (amount of crop) that they get from every square meter. They can augment this yield data with a variety of other information. For instance, they may also perform a detailed soil sampling survey to determine the soil’s nutrient mix in different areas of the field. Plant health can be assessed in part by plant coloration, so some farmers will purchase satellite or aircraft flyover imagery enabling them to determine the health of their plants at various times during the year. Of course, all of this additional information goes into the computer mapping software.

Based on their collected and mapped (georeferenced) data, farmers can generate prescription maps which specify how much fertilizer to apply in each region of the field, how densely to plant seed in that region, and so on, in order to optimize yield and minimize unnecessary chemical applications.
To take optimal advantage of prescription maps, many modern farming implements are computer-controlled. Planters, as the name suggests, put seed in the ground. A planter such as the one shown in Figure 2 may feature independently controllable row units, enabling each unit to be turned on and off, or have its planting rate adjusted, independently.

Given this sort of planter, the prescription map is loaded into the computer on the tractor, and the tractor driver simply..drives. The driver steers the tractor, the tractor pulls the planter, and the onboard computer controls the seeding rate based on where the planter is in the field. The computer also tracks where seed has already been applied, so if the driver has to drive through already-seeded territory, it doesn’t get double-seeded.
The same strategy is applied in other crop maintenance activities such as fertilizing and other chemical applications. The computer monitors the vehicle location and ensures chemicals are applied only where prescribed, in customized doses tailored to the specific area. This has cost benefits to the farmer — less chemicals used is dollars saved — and it also has environmental benefits, since less chemicals used is less chemicals at risk of leeching into the surrounding ecosystem.

Cooperative autonomy

When putting seed in the ground, and later coming back to harvest it, it’s important that the harvester follows the same path as the planter did months earlier. And of course, the harvester and planter know where they are the same way that your smartphone knows where you are: GPS.
You may have noticed, however, that your cell phone can be off by quite a bit. The difference with farming vehicles is that while the GPS unit you have in your phone or your car knows where you are to within a few yards, a high-precision augmented GPS in a modern tractor knows where it is to within a couple of inches. This accuracy has profound consequences.
First, it allows a tractor driver to reproduce a route over and over. At planting, seed goes in the ground. When treating, fertilizer is applied directly to the seeded area — without being applied to the unseeded territory between the rows. Finally, at harvest, the driver reaps with high efficiency by being just as accurate coming along the rows in the harvester.
Historically, the precision-guided driver was assisted by a “lightbar” — a line of LEDs that indicate in real-time whether the vehicle is on track or whether a steering correction is needed. Nowadays, an advanced tractor just drives itself along the route.
Auto-steer systems are available for a variety of tractor models, both as built-in and as after-market additions. Most current auto-steer systems can only drive the rows, requiring driver intervention at the end of each row, but advanced systems from some vendors can now handle certain simple turns. Even with auto-steer, a driver is still required to watch for obstacles and monitor the equipment, although the work is a lot less fatiguing when the driver can go “hands-off” for long rows. While the farmer is still setting the throttle and looking out for collision, the system is inarguably autonomously driving itself.

Prepared for full automation

This says a lot about farmers as future users of more completely automated systems. On farms that have embraced precision agriculture:

• The farmers are tech-savvy computer users.
• They survey their fields with precision.
• Their tractors are already partially drive-by-wire, meaning that a computer can already control key functions such as steering.
• Their tractors are equipped with high-precision GPS systems.

Given this baseline, transitioning to full autonomy is relatively straightforward, using off-the-shelf parts and advanced software.

Case study: Kinze Autonomous Grain Harvesting System

Kinze Manufacturing makes grain carts and planters for row crops. For the last few years, Jaybridge Robotics has been working with Kinze to automate tractors pulling their grain carts to produce the Kinze Autonomous Grain Harvesting System, shown in the opening Video and in Figure 3. In this case study, we’ll take a closer look at how the Kinze system builds on existing technologies and Jaybridge’s software.

A high-level overview Jaybridge Robotics’ software is shown in Figure 4. Our software takes advantage of commodity components to perform key vehicle automation tasks including:

• User interface enabling the user to perform the workflow.
• Vehicle path planning.
• Vehicle control, including steering, brakes, throttle, etc.
• Navigation
• Obstacle detection
• Inter-vehicle communications

Let’s look at how these elements are realized in the Kinze Autonomous Grain Harvesting system.
The user interface, shown in Figure 5, runs on a touch-screen Android tablet. Working with Kinze, four primary workflow elements were identified. In Offload, a grain cart drives in tandem with a combine while the combine simultaneously harvests and dumps crop into the grain cart. In Follow, a grain cart follows along behind a combine, for instance when the combine transits a narrow area. In Park, a grain cart drives back to a designated parking area, where it meets up with a semi which will transport the grain onward. And in idle mode, of course, the grain cart idles awaiting further instruction. Those key workflow elements are realized in the major buttons down the right-hand side of the screen, while additional capabilities such as manual crop editing and obstacle denotation are provide along the bottom.

Path planning, vehicle control, navigation, and obstacle detection all take place in real-time on the embedded computer onboard the tractor towing the grain cart.
The path plan adapts in real-time as the combine moves – keep in mind that combine motion moves not only the grain cart’s destination (in follow or offload modes), but also clears crop, creating additional drivable area. The plan may have to feature complex maneuvers, e.g. Figure 6 where harvesting is taking place in a terraced field. The path planner relies on the navigation system identifying the grain cart’s position, orientation, and velocity. When tandem-driving with the combine for offload, it also relies on high-speed communications between the vehicles to exchange position information. The path planner must continuously consider vehicle position and the drivable area map, as well as the vehicle’s physical capabilities.

Vehicle control also takes place in real-time, ensuring that the vehicle follows the planned paths. Like path planning, control runs on the onboard embedded computer, synchronizing throttle, brakes, and steering to achieve the desired path.
The navigation system fuses data from the factory-standard high-precision GPS system with other vehicle information to provide an extremely accurate estimate of vehicle state.
The obstacle detection system relies on a spinning laser range finder (LIDAR) and automotive RADAR more typically used for adaptive cruise control. Data from both sensors are fused for enhanced detection capability.
Inter-vehicle communications take place via two different channels. At longer ranges, grain carts and combines communicate via cell data, taking advantage of pervasive cell coverage extending ever deeper into the heart of farm country. At close range, and especially when tandem driving, a short-range high-bandwidth radio is used to exchange data to coordinate driving.
It’s important to note that the hardware components are commercial-off-the-shelf (COTS) parts: from the embedded computer to the LIDAR to the cell modem, the technology exists today at very reasonable price points. Jaybridge’s software transforms them from a collection of parts to a fully automated grain harvesting system.

Reliability

Industrial machinery has to be reliable. Farming machinery is no exception. So a key aspect of Jaybridge Robotics’ work is ensuring that automated vehicles, and the software controlling them, are reliable. Jaybridge relies on a number of techniques including formal code inspection, unit testing, regression testing, and large-scale simulation (see Figure 7) to validate software before it goes onto real hardware. Simulation, in particular, is a potent tool in our arsenal: it gives every Jaybridge engineer a complete system to work with, without having to find parking for a bunch of tractors.

Ongoing development

The Kinze Autonomous Grain Harvesting system was unveiled to the public in 2011. In 2012, multiple systems were put into the hands of real Illinois farmers for the fall corn and soybean harvest. In 2013, systems are once again working the harvest, with increased capabilities and ever-greater robustness. As we continue along the technology roadmap, we are looking forward to further enhancing the capabilities and robustness of the Kinze Autonomous Grain Harvesting System.

Targeting a sustainable presence of humans in outer space will require solving air, water, energy, and food supplies within a few thousand cubic feet surrounded by vacuum. What seems at first sight to be a problem of an apocalyptic, remote future reveals itself as the grand challenges of our civilization in a nutshell. This article argues that space exploration can be one of the main drivers to revolutionize sustainable agriculture on earth.

First, the agricultural industry has classically not been the driver, but on the receiving end of innovation in automation. Current economic drivers promote increasing the size of farm equipment and mono-cultures, which are more suitable to automation. Thinking about solving food production on a space-ship, Mars-colony or city-scale, which have opposite requirements than industrial agriculture, makes it clear that solutions will not come from incremental changes to the current system, but require a disruptive approach that has very little to do with current agricultural practice. Second, sustainable agriculture is a systems challenge that does not stop at innovation in automation, but also requires advances in renewable energies and integration into water and air management to be successful, in particular when considering integration of agricultural production into urban environments. Third, advanced life support systems are not only mission-critical for long-term exploration missions, but the National Aeronautics and Space Administration’s (NASA) support strongly hinges on the perceived value of its mission; using space exploration as a driver to solve our most pressing grand challenges is a strong narrative to gain public support.

A brief history of agriculture

Since the advent of agriculture around 12,000 years ago, humans have developed a highly sophisticated system for global food production with the most rapid technological advances occurring during the first half of last century. In the 1920s, agriculture had not only adopted new machinery, but also the financial, cultural, and ideological apparatus of industrialism. This process has led to farm equipment of ever-increasing size, modern plant breeding programs, the use of synthetic fertilizers, delivery of water via irrigation systems, and the use of pesticides to control crop herbivory, which have all contributed to a tremendous increases in crop yield. For example, corn yields in the US increased approximately 400%-500% from 1940 to 1997. Together with consolidation of small farms into larger ones, the number of people that a single farmer in the US provides for increased 9-fold from 15.5 people per farm in 1950 to 140 people per farm in 1997.

While providing immense cultural benefits, these developments came at tremendous environmental costs. Increased fertilization has led to excess nitrogen and phosphorus in the water systems impacting both human health and the integrity of aquatic ecosystem, and also to increases in nitrous oxide production (a potent greenhouse gas). More recently, there is also an increasing awareness that the global supply of phosphorus, which is a non-renewable resource but an essential plant nutrient contained in most synthetic fertilizers, is expected to peak mid-century and decline thereafter.  Also, cultivation of just a few crops (corn, soybeans, hay and wheat make up 68% of farm land in the US) in ever-increasing monocultures and the resulting lack of plant diversity make these systems vulnerable to large-scale pest outbreaks. Finally, the high specialization on certain crops in different parts of the US – which is the result of industrial streamlining the process – requires considerable transportation cost, and might become infeasible with increasing cost of oil.

Taken together, there is a critical need to develop agricultural practices that deliver water and nutrients in a manner that minimizes losses from these systems, while creating an ecologically resilient agricultural system that can withstand or quickly recover from disturbances such as pest outbreaks, for example by using companion plants and poly-cultures.

Advances in robotics can decrease detrimental effects of farming by precise administration of water and nutrients and inter-cropping, while bringing agriculture closer to consumers. Besides being able to provide individual plants with the required resources on an as-need basis, small-scale robotic platforms are not limited to operating on fields, but could also reclaim urban environments that are currently deprived from agriculture and horticulture, such as within offices, shopping malls or on roofs. A concept drawing of a team of robots cultivating plants in a shopping mall is shown in Figure 1.

It is worth noting that neither agriculture nor horticulture have ever been the main drivers for innovation in automation. Rather, industrial methods and techniques were adapted to agriculture long after they have proven successful in other trades. This trend seems to be persistent still today. For example, automation in agriculture is enabled exclusively by the availability of small-scale and cheap computation, which has been driven by the electronic spread-sheet. Similarly, disruptive actuation and sensing technologies that have the potential to bring agricultural automation to a new level are being developed in orthogonal markets, such as the gaming industry, manufacturing, or construction.

Indeed, major industrial players in precision agriculture support their technical innovations by developing for the construction industry, e.g., large earth moving equipment, which provides higher margins than agriculture or horticulture. Based on this history and lack of economical drivers that will require industry to leap from large-scale automation to urban-scale precision agriculture, I argue that truly disruptive innovation is more likely to be developed for niche applications, such as space exploration.

Air, water, energy and food in a nutshell
Going to Mars is in the tradition of setting foot on the moon, exploring the West, and ultimately the very exploration that led to the discovery of America. All these missions have in common that they are variants of the “knapsack problem”. Explorers need to decide on a combination of provisions and tools that allow them to maximize exploration and minimize risk of failure. Parameters of this problem are the set of brought-along goods, available resources harvested on the way, and the constraints of vessel size. Larger vessels allow more goods to be brought along, but also require larger crews to maintain them, again requiring more resources.

A space mission adds an additional complication as the launch mass is limited by the amount of fuel. The larger the rocket, the more fuel is needed, in turn requiring more propulsion to increase launch mass. This optimization problem currently limits how long we can sustain ourselves in space and therefore where we can go and what we can do there. While advances in agricultural practices and transportation systems have solved this problem for humans who have now permanently settled earth’s most remote spots, this approach is about to reach its limitations on earth and does not extend to space.

Considering the vast emptiness of space (and the scarcity of resources on other planets), solutions to this problem will require us to make better use of resources found in place, and to think about how to recycle them. For example, food can be grown and fertilized from “black water” and help to turn toxic carbon dioxide into carbon – to remain in the plant – and oxygen. While maintaining a fully enclosed ecosystem that is significantly smaller than our own planet and can support human beings has not been accomplished yet, doing the math on launch mass and potential yield has shown that growing food in space becomes advantageous for missions exceeding two years in space. For missions shorter than that, the additional launch mass and space of the tools and provisions that plants require is better used by bringing additional resources. An alternative scenario is to launch life support systems to arrive before humans do (Figure 2). In both cases, automation is necessary as the use of humans to perform these tasks is highly inefficient in a space scenario.

A major payload driver for autonomous food production is the additional required water resources. Yet, after reaching a critical mass, plants are an excellent natural system for converting black water into drinking water. Hereby, plants consume the water via their roots and return purified water into the local atmosphere via evaporation. Although small-scale systems to replace waste-water treatment plants in remote areas have been successfully demonstrated, additional research is needed to implement such systems in a self-contained, easy to maintain, fashion. Future long-term space exploration missions are likely to depend on a combination of bio-chemical processing and biological filtration, requiring a systems view and increased understanding of the micro-biology of the underlying processes. Although other planets provide massive reservoirs of frozen water in their pole caps and under their surface, water shortage has become a major problem in the US west of the continental divide and large areas of the world are subject to desertification, making water shortage a global problem, and requiring drastic increase and decentralization of processing. While water shortage for now mainly affects our quality of life, water conflicts have been linked to the Rwandan genocide and the war in Sudanese Darfur.

Plants also have the capability to absorb carbon from the air and releasing oxygen. On earth, this task is to a large part accomplished by tropical rain forests, which are responsible for roughly a third of the earth’s oxygen turnover. For space exploration, plants alone are not sufficient, requiring machines to scrub carbon dioxide from the atmosphere. While regenerative carbon dioxide scrubbers – a process mainly requiring electricity – have been in use on the now decommissioned space shuttle, terrestrial applications include carbon dioxide reduction of power plant exhausts. Albeit feasible, current technology is not cost-efficient to deploy and to operate, mainly due to the lack of economical drivers. This research would be desirable, however, as carbon dioxide emissions are believed to be the main driver behind man-made global warming, with its long-term effects yet to come.

Lighting for food production and photosynthesis, water purification, and carbon dioxide scrubbing will require additional energy, further driving the required launch mass of an exploration vessel or station equipment. Energy is available in effectively unlimited quantities from the sun, but requires efficient conversion. On earth, solar power is also an attractive alternative energy source for earth-based applications and could drastically reduce our dependency of fossil fuels, which are both a source of international conflicts and carbon dioxide emissions. Current solar cell technology is not yet competitive with electricity generated from fossil fuels and nuclear power, requiring additional research into reducing production cost while enhancing efficiency. A space mission could be a significant driver for the latter without being limited by the economical drivers of fossil fuels that dominate next-generation solar cells that industry deems economically viable.

Space exploration as a driver for our most pressing grand challenges
Space exploration has historically been a geo-political, strategic instrument. Showing the ability to precisely place an object into lower earth orbit or onto the moon demonstrates the capability to project power at long distance with high accuracy. In addition to leading to game-changing military capabilities, space exploration has transformed life on earth. Sputnik, the first radio satellite, led the way to worldwide telecommunication and global positioning. Sputnik also spurred a space race, leading to launch systems of increasing size and precision, eventually culminating into the Apollo program, the first human on the moon and the strategic defense initiative (SDI), also known as “Star Wars”. Albeit the collateral science and engineering benefits of these missions are beyond doubt, they became increasingly less tangible.

NASA has recovered from this by focusing its missions on atmospheric sensing, revolutionizing the fields of geology, agriculture, archeology and many others, and space exploration leading to jaw-dropping and deeply inspiring photographs of outer space that have fundamentally changed our understanding of the universe and our role therein. Nevertheless, the cost of these missions seems to less and less justify their benefits.  With the industrialized nations accumulating prodigious amounts of debt and the developing countries fighting over water and food, understanding the origins of our universe and setting foot on other worlds becomes more and more a secondary goal in public perception.

NASA is currently rallying interest for a “planetary defense” mission with the goal of robotically bringing asteroids into a near-earth orbit where they can be used as a shield to deflect asteroids such as the one grounded in Russia in the spring of 2013. Albeit such a mission supports the geo-political narrative of the US as global guardian and would lay the technical foundation for a paradigm shift from earth-launched to space-launched exploration, the actual risk of an apocalyptic asteroid impact is minuscule compared to risks arising from political instability and resource problems elsewhere in the world.  Therefore, it is unlikely that such a mission can inspire the same national energy that the space race did, and which is imperative for the success of a manned mission to Mars.

Instead of motivating space exploration with geopolitical advantages and science outcomes that are inaccessible, or even moot, to the general public, I argue to identify missions whose success is aligned to solve mankind’s greatest challenges: food, energy, water and air.

Next steps in robotic research

With growing food in space not on the critical path of missions immediately ahead, but a long-term requirement, research in space-based agriculture should focus on the following three fronts: increasing our knowledge of in-space plant growth, solving the key perception and manipulation challenges of plant maintenance, and understanding the impact that observing, maintaining and eating plants has on humans in the isolation of space. These three thrusts are closely inter-related and can benefit from automation. Indeed, plant growth experiments are recurrently undertaken on the ISS and could tremendously benefit from automation. For example, light-weight, low-energy robotic arms could enable scientist to systematically sense within the plants’ canopy, and automated management of air, water, and nutrients on a per-plant basis would allow us to better understand how the space environment affects these parameters.

As fully autonomous plant maintenance requires solutions to a series of hard problems in perception and manipulation, initial focus should be on remote operation of the growing process (Figure 3, left). Devising a system that solves all the mechanical, user interface and communication challenges that would allow to grow plants (from seeding to harvest and re-planting) can serve as the basis for further developing automation and could motivate its own mission such as deploying a greenhouse container to Moon or Mars.

Concerning human-factors, there is strong evidence that humans living in the isolation of space will drastically benefit from interactions, including taking care of, harvesting and eating, of plants. Active research challenges here are how to design this experience to be most beneficial for the astronauts. This includes not only identifying care-taking tasks that the astronauts actually enjoy, but also providing solutions to automating those tasks that are undesirable or induce additional stress. In the long run, remote operation will be more and more subsumed by actual autonomy. Here, key challenges are automatic assessment of plant status, manipulation of soft, flexible objects, and dealing with adverse environmental conditions such as mud, water and unpredictable growth that can quickly jeopardize a running system.

Summary
Our agricultural system is about to reach its limitations, with demand for its products to outpace availability of resources. Space exploration is concerned with similar challenges, albeit at an extreme scale: water, energy, and air need to be turned into food in a nutshell in a most sustainable way that minimizes the influx of external resources. While industrial agriculture addresses its challenges by ever-increasing monocultures that are economical to automate, the needs of future urban centers on earth and colonies in outer space require an opposite approach: small-scale precision agriculture that provides care to plants on an as-need basis and that can integrate with water purification and air revitalization needs of its consumers. As the technological leap this approach requires is gigantic, it is unlikely to be performed by the agricultural industry, whose economics exclusively support incremental changes. This is not the case for space exploration, which can – given strong public support of its mission – support disruptive technological advances such as sending a man to the moon and eventually allow us for sustainable existence on other planets. This is a challenge that dwarfs sustainable living on earth.

Link to audio file (31:48)

In this episode Sabine Hauert speaks with Jorge Heraud, CEO of California-based startup Blue River Technology which brings together computer vision and robotics to automate agriculture. Their first robot LettuceBot targets the state’s #1 vegetable crop. Its task is to thin rows of lettuce in fields. This involves selectively removing some of the plants by spraying excess fertilizer on them, thereby avoiding overcrowding while fertilizing nearby plants. The tractor-mounted robot is already being rented out to farms across the state.

Heraud tells us about the challenges in robot vision and the rapid growth of Blue River Technology. He shares his hopes to apply their technology to other tasks in agriculture, and crops with different vision challenges. Finally he explains how this technology will transform the classical workforce on farms.

Jorge Heraud
Jorge Heraud is CEO of Blue River Technology. Before co-founding the company with Lee Redden, a fellow graduate student at Stanford University, Heraud worked in precision agriculture as Director of Business Development at Trimble Navigation. At Stanford he completed an MBA at the Graduate School of Business.

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There are bushels of folks out there now spending money and putting time in finding niches for unmanned systems that already exist. You may be saying, isn’t that what we should be doing? Possibly, but first we have to understand that the idea of carrying sensors on aircraft is not revolutionary, it has been going on for years. Drones too, but we are supposed to act like it doesn’t happen because FAA policy says so.

At the lower end of the data collection spectrum of course is photography. Pretty pictures may be enough to satisfy some. However, others may need just a little more data to make all of the regulatory hassle and cash outlay worthwhile. Let’s use the pick-up truck analogy that has been bandied about. Sure you can haul your roller-skates, but does the added expense and cost to operate justify the outlay?

The broad brush of application lacks definition and is almost as wild and vague as the general public’s capabilities concept of drones. Yes, they can do a lot of something, but what can they really do, and more important still, what is the value proposition? There is a lot of cheap data, but cheap is not always good if you are the one that has signed up to supply it.

This is where the public drone conversation meshes on both the pro and con side of the issue. Plenty of people mistakenly assume that many of these sensors are revolutionary, and cannot, nor have they ever been flown until the advent of the drone. Not so much, as there are companies that have been doing this type of work for many years, even employing kites and balloons.

Even those who do not possess an advanced degree in Thinkology understand the prima facie value proposition: lessen farm inputs while increasing farm outputs. Still, many for some reason totally disregard the fact that you can hire, or buy a single engine aircraft that affords the capacity of flying almost anywhere in the National Airspace System (NAS). So, for +/- $100 per hour you can fly an array of different sensors completely unfettered by purely arbitrary regulatory criteria.

For reference checkout www.willflyforfood.com.

I know you’re thinking… “ok that’s great, but this isn’t the same old routine… I’ve got a new angle on this agrarian gold rush thing.” You’re not alone, as I get the barn-full-of-cash call from all sizes and shapes of companies evaluating that very same proposition. I usually start with, “What system are you using, and why?”

There are a few items that should be contemplated. One, how many drone amateurs will be out there trying to eat your lunch on what will amount to the pool route business model. Those in the business already got a taste of it prior to the policy clarification of 2007. How many UAV companies can one town support before the prices start to drop? In mid 2005 and 2006 scads of folks jumped in, as they were now able to tell their wives that this wasn’t just some self-indulgent hobby they were dumping thousands of dollars into anymore. Second, the regulatory model had folks flying well beyond what is now globally accepted as VLOS (Visual Line Of Sight). If a pilot is good, that envelope allows for approximately 300 +/- acres (standing in the middle of the crop with a certificated spotter), for the venerable point and shoot camera, less for sensors that have smaller fields of view. That will require that you pack up and move the operation at least once to capture the average U.S. farm at around 441 acres.

Now, I’m not saying that you should bring the Wranglers and boots back to Sears… UA’s and precision Ag do hold a lot of promise. However, before you run out and purchase equipment for an illegal business with legally earned money, you may want to stop, step back, and go over the plan one more time. I hate to be the one that rains on the scarecrow, but do yourself a favor and take the time get educated.

It seems everyone these days is rolling out grandiose Precision Agriculture business plans. These plans would raise a brow with those that have already been working the rows. Some of them are so eccentric on both ends of the spectrum that I wonder if they’ve been collaborating with the scarecrow.

Incautious plans that are built around the purchase of surplus military systems including, but not limited to, Shadows, Global Hawks and or Predator variants should be reevaluated by independent third parties. Investors and other would-be business people that believe the regulatory side of things will just “work themselves out”, might be better off just getting into another business altogether.

The tangible promise to be realized with unmanned aircraft has always been low cost and self-guided remote sensing. Separating the wheat from the hype chaff may be a more profitable endeavor in the long run.

With the global market for unmanned systems in agriculture on the rise, companies that make robotic ground and air systems are paying close attention.

More than 25,000 “field” or agriculture robots will be sold by 2015 — about the same as robots for military use, according to the International Federation of Robotics. Together, defense and agriculture make up the lion’s share of the nearly 94,000 “service robots for professional use” that the IFR believes will be sold in the next couple of years. Defense and agriculture are by far the two largest categories in IFR calculations, with robots for things like logistics, medicine and rescue coming in well behind.

IFR statistics for 2011 sales of “professional use” robots — as opposed to robots for the industrial sector — offer a snapshot of this market. Overall unit sales in 2011 were up 9 percent over 2010, with unmanned systems for armies around the world coming in at 6,570, or 40 percent of the total. Right behind were sales of unmanned systems for agriculture at 5,000 units, or 31 percent.

Going Commercial
For companies like Harvest Automation, such numbers confirm that they’re on the right track. The Billerica, Mass., company was founded in 2010 to look for ways to match robotic technology with sectors that still require difficult, repetitive manual labor, says CEO John M. Kawola. Meatpacking, warehousing, construction and a host of other areas were scrutinized.

But, he says, the founders “pretty quickly honed in on agriculture,” because even though there have been significant advances in automation in “big ag” — wheat, corn and soy, for instance — “there still are large sectors of agriculture that rely heavily on manual labor.”

One is horticulture, a category of specialty crops, which, according to the U.S. Department of Agriculture, is defined as “intensively cultivated plants that are used by people for food, medicinal purposes and for aesthetic gratification.” This includes cut flowers, evergreens and bushes — “anything that is grown and sold in a container,” Kawola says. It’s “the nursery and greenhouse sector.”

But horticultural work today, “from planting to maintenance to harvesting, is being done the way it was done 20 years ago, 100 years ago.” Harvest Automation’s goal, therefore, is to use robotic technology to address material handling, harvesting, information gathering and sensing, and information management. It’s worth the effort because the wholesale horticulture market in the U.S. is huge, $17 billion. The crop value per acre is also huge — $15,000 for blueberries, for instance, compared to about $500 per acre for big ag crops like corn and wheat. In addition, it’s a much more continuous business — while big ag crops are planted and harvested once a year, horticulture crops can turn over every six weeks or so.

All this presents several possibilities to robot makers. The one initially chosen by Harvest Automation is to take the relatively easy approach of picking up and moving containers or potted plants. Its first product, the HV-100, is designed to do this job in nurseries and greenhouses. Kawola says it’s easy to operate by farm workers, and, because it’s relatively small, weighing about 80 pounds and standing two feet tall, it’s a safe working companion. He says it can work individually or in teams, depending on size of an operation. A video of several of the robots at work shows their movements to be similar to those of iRobot’s household Roomba vacuum cleaner. In fact, says Kawola, Joseph Jones, cofounder and chief technology officer of Harvest Automation, was one of the inventors of the Roomba when he was at iRobot.

Another company, Robotic Harvesting LLC of Simi Valley, Calif., has chosen to approach the market with a technically more complex system, one that harvests strawberries. The idea, says CEO Joe Wickham, is to develop a robot that can “automatically go up and down the fields picking strawberries. It’s still in the prototype phase. It’s a complex piece of machinery. It’s not something that can be done overnight, but we’re very interested in bringing this to market and getting commercial acceptance for it.” The recession slowed interest in the project and has prompted Robotic Harvesting to put it on the back burner.

Wickham acknowledges that, at least for now, people are more discerning than robots when it comes to identifying strawberries that are not fully ripe or misshapen. On the other hand, he says, advances in sensors and image processing software, “like disparity mapping and other algorithms that are useful for these types of complex vision tasks,” are making the technology more feasible. Meanwhile, Wickham says, some universities are studying the idea of developing a breed of strawberry plant that’s nearly leafless, “and the thinner the leaf canopy, the easier it is to robotically harvest the berry,” Wickham says.

Challenges For Flight
While companies like Harvest Automation and Robotic Harvesting are dealing with their own sets of challenges in agriculture, companies that want to use robot aerial systems in this market face others. One big hurdle is approval by the Federal Aviation Administration. It’s now illegal to fly unmanned aerial systems for commercial purposes in U.S. airspace. Assuming the Federal Aviation Administration approves low-level, line-of-sight operations — like those applicable to the monitoring and spraying of crops, for instance — analysts expect the market to boom. An AUVSI report in March 2013 says that during the period 2015-2025, integrating UAS into national airspace will contribute $82 billion to the U.S. economy — a whopping $75.6 billion of which will go directly to agriculture. Another $3.2 billion will boost the public safety sector, and the remaining $3.2 billion will go to the “other” category. More than 100,000 jobs would be created.

The 38-page report, “The Economic Impact of Unmanned Aircraft Systems Integration in the United States,” says unit sales of UAS will rocket from just under 40,000 in 2015 — when FAA is expected to give its approval — to about 160,000 by 2025. Even so, the study’s authors take what they say is a conservative approach, using “100,000 unit sales per year as a conservative benchmark.” One of the authors, Bijan Vasigh, a professor of transportation at Embry Riddle Aeronautical University in Daytona Beach, Fla., says the numbers could be higher, or lower. “We are really projecting what will happen in a normal scenario. If the scenario is more conducive for [unmanned aircraft], the impact would be much stronger.” At the same time, unforeseen circumstances “could impede progress.”

Some aren’t sure how the FAA will rule, but Vasigh is optimistic. “Frankly, I don’t see that much problem with FAA. I’m a little bit positive on go ahead, especially at lower altitude.”

Optimism, with a bit of caution, is also expressed by Young Kim, general manager of BOSH Precision Agriculture, Newport News, Va. BOSH, which has been oriented to the military UAS market, is shifting its sights to UAS for agriculture because the market is big and steady — as opposed to defense, which is big but probably won’t be steady forever.
“As an entrepreneur, I’m an optimist,” Kim says. At the same time, he sees no large, single, organized group engaging the FAA. “If we can get the farmer, UAV vendors, universities, economic development people, ag trade organizations,” like state cotton and peanut commissions and citrus growers, all addressing the FAA with a unified voice on safety and certification and policy, possibly with AUVSI participation, success would be more certain.

Foreign Expertise
Japan is well ahead of the U.S. in the use of unmanned aerial systems for agriculture. In 1983, the Japanese government asked Yamaha to help it develop an unmanned helicopter for agricultural duties. The country was faced with an aging farming population and wanted to make things like spraying more efficient, according to Steve Markofski, a U.S.-based new business planner for the company. In 1991, he says, Yamaha began to market its first ag-oriented unmanned helo, the R50. Today, 2,150 Yamaha RMAX unmanned helos spray about 2.5 million acres a year in Japan, covering about 40 percent of the country’s rice paddies. The government backed the idea from the beginning, with the Japanese department of agriculture responsible for regulation.

Yamaha hopes to translate its success in Japan to the U.S. One area that it thinks is promising is spraying vineyards. Yamaha is testing the idea, under a certificate of authorization from the FAA, by flying over vineyards in Napa, Calif. They’re about the same size as rice paddies in Japan, five acres or so, and therefore a good match to the RMAX’s four-gallon chemical payload capacity, Markofski says. Tractors, traditionally used in Napa vineyards, can spray about two acres an hour, but RMAX can do 12 to 15 acres an hour. And with RMAX, Markofski says, there’s no soil compaction, no crop damage, the operator is not exposed to chemicals and it is safer, because he doesn’t have to drive in challenging terrain, like slippery hills. “We’re looking at areas [in the U.S.] that we think align directly to the RMAX, and vineyards is one that jumped out at us,” Markofski says.

Canada’s MicroPilot also is waiting for a green light from the FAA. The company, based in Winnipeg, Manitoba, is a leading manufacturer of UAS autopilots as well as the maker of CropCam, a camera-toting fixed-wing UAS that has made a name for itself in crop monitoring. CropCams have been sold in places like Kazakhstan, Southeast Asia and South America, but approval to fly in the U.S. would be a big boost, says Pierre R. Pepin, vice president of sales and marketing. He says one business model might be to work with those who sell pesticides, for example. They also could sell UAS services. A customer would therefore not only get a product from the seller, but also precise images of his crops.

“Once you’re allowed to fly,” Pepin says, “it doesn’t take a whole lot of imagination to see how you can use those little birds.”

[This article first appeared in AUVSI’s Mission Critical‘s agriculture issue.]

No, this is not about shapeshifting robots, come to save or destroy Earth. It is about transforming the contexts within which robotic technologies are applied, and about practicing robotics with the intention of bringing about transformational results. In some cases this means finding better ways of accomplishing the same ends as before. In other cases it means pursuing ends that were previously unachievable. It hinges on the recognition that robotics is a revolutionary development, on the order of fire or writing, with the potential to transform everything it touches.

We can already see some early examples of how transformational robotics can be. In medicine, automated lab equipment long ago made the comprehensive metabolic panel screening test affordable, and surgical assistant robots have shown considerable promise. In aviation, modern airliners are capable of flying themselves from takeoff to touchdown, and remotely piloted or autonomous aircraft remove biological constraints on maneuvers. In space, were it not for robotics, most of what we know about the solar system, including our nearest planetary neighbors, Venus and Mars, would be limited to what we could learn through the use of telescopes, and there would be no serious talk of mining asteroids.

Before jumping in to how this applies to agriculture, allow me to make one additional point about transformational robotics in general, which is that you can ordinarily expect a transformational application to make more intensive use of technology than an application that simply automates some familiar process or procedure – being mechatronically more complex, or expending more cpu cycles per unit of physical energy used, or both. This is because, in transformational applications, the machines will typically be taking more into account and choosing from a larger, more flexible repertoire of deft actions (precise in context, although not necessarily especially precise in the sense of repeatability).

The key to both understanding and making a case for transformational robotics (in pursuit of private investment or government funding) is to focus on the goals to be served, rather than on current approaches to achieving them. (‘People have to eat, but we also need to preserve and improve fertile land and provide space and habitat for native species.’ versus ‘How can farming as we know it be made more efficient?’) Those current approaches may be ruts, out of which the transformational application of robotic technologies can lift us. In this regard, it is important to include goals we wish to serve which are currently being ignored or given short shrift, whether because no business case can be made for them at present, or because the cost would be prohibitive. Much of the added value of transformational robotics stems from serving ends to which we have previously given little more than lip-service, often for essentially zero additional cost.

Now let’s consider the application of robotics to agriculture, beginning with the current state of the art. The most common use of automation in agribusiness has traditionally been in processing plants, after the food has left the farm, although more and more such operations are being installed on the farm. For plant products and eggs this begins with cleaning and sorting, and any sort of product may be automatically packaged or bottled and packed for shipment.

On dairy farms, robotic milking machines are becoming increasingly common, essentially allowing the cows to milk themselves. In the fields, new tractors now commonly come with GPS-based automated steering, allowing them to make more efficient use of the fuel they burn by reducing overlap from one pass to the next and to preserve soil conservation measures, such as terraces, by following contours more accurately. Combine harvesters may have both automated steering and automated ground speed, to keep them working near their peak capacity without pushing material through so quickly that they become clogged. Spraying equipment, whether tractor-mounted or self-propelled, can now meter out fertilizer, pesticides, herbicides, and fungicides, applying more on parts of the field where the need is greater. A recent development is planters that place seeds more deliberately than in the past, achieving a more even stand using less seed.

While perhaps not yet commercially available, automated pruners for vineyards and automated fruit harvesters are in an advanced stage of development, and should soon be commonplace. And last, but far from least, UAVs, commonly known as drones, can make an abundance of valuable information available to farmers, and the business of providing such services is set to take off like a rocket just as soon as the FAA determines that the time is right.

Next, it’s time to engage our imaginations, as we consider other ways in which robotics might be applied to agriculture, beginning with the main obstacles. One huge challenge, demanding a great deal of further research, is empowering machines to make sense of complex biological environments. As stated in a recent news item from Washington State University:

When apples are in clusters or obscured by leaves and branches, a robot requires complex algorithms and long computational time to identify them.

If this is true of fruit hanging from trees in orchards, more or less in plain sight, it must be even more true of ground-hugging herbaceous plants, the main stems of which may be hidden in deep shade, and still more true when those herbaceous plants occur in polyculture mixtures.

Another such challenge is enabling machines to nimbly reach through foliage without damaging it. For any device that isn’t itself ground-hugging, this is essential for weeding and for planting a second or third crop under an existing canopy of leaves. While much of this work might be accomplished by small devices, moving across the soil surface, there are tasks for which (pruning) and circumstances in which (soggy soil) devices suspended from above the leaf canopy would be more effective. The word ‘nimbly’ suggests both speed and a light, deft touch (sensory-driven precision), and both are intended here. Machines of a scale to support one or more suspended robotic arms, even very light arms, while less massive than most conventional equipment, would still represent a significant investment, and would need to operate quickly to be cost-effective. Quick motion means even quicker electronics to plot paths, gauge the forces needed to produce them, updating these with corrections as deviations are detected, and compensating for environmental factors such as wind-load, slope, the weight of a fruit being picked, and so forth. Quick motion also means being quite certain about the nature of the objects in one’s immediate vicinity, since they might include livestock, cherished pets, or human beings.

Given solutions to these primary challenges – and autonomous operation – in addition to planting, weeding, and harvesting, robotic machines might track the development of individual plants; detect nutrient deficiencies, toxic conditions, disease, and infestation; selectively prune affected leaves or branches, or selectively remove whole plants; collect plant material for shredding, anaerobic digestion, and/or composting; lay out soil sensors or tubing for drip irrigation; attach vines to elevated supports; employ mechanical methods of pest control and deploy biological pest controls. Beyond this they might also engage in crop development, by collecting pollen from certain plants and applying it to others, and through the collection of seed from plants with preferred characteristics, selecting from those based upon nondestructive testing of individual seeds. They could even monitor and to some extent manage populations of wild animals, such as mice, rabbits, and deer. In principle, there is no farming task which machines could not be made capable of performing.

Patch together enough such capabilities, at reasonable cost, and sooner or later you arrive at a tipping point, beyond which it makes far more sense to think about what you would like to see done, and how robots might be made capable of accomplishing it, than to take the more familiar approach of automating conventional methods. On the other side of this tipping point, agriculture may look very different from how it appears today. If the machines operate autonomously, then one farmer can conceivably have many of them all working at the same time, even working around the clock through the peak workload of the season. If there are many of them, working long hours, they can be smaller, lighter, and slower than conventional equipment, and, partly due to economies of scale in their manufacture, far less costly. If they are smaller, slower, and more numerous, they can take a more detailed approach to manipulating their environments. Given sufficiently detailed manipulations, it becomes possible to adapt the best practices of gardening, applied on a grand scale. Also, given this data/detail-intensive approach, production can be closely linked to changing market conditions, helping to keep the operation profitable.

Besides the needed research mentioned above, the ground floor opportunities in this scenario run the gamut of robotic technologies, from making equipment safely mobile through densely planted fields to sensory hardware, to processors, coprocessors, and algorithms for sorting out the sensory data, to end effector design and capability modeling, to force-control motion planning, actuation, result monitoring, and heuristics, and on and on.

The singular reality that makes such a grand effort more than a fool’s errand is the scale of agriculture, and its potential to jumpstart a larger, more robust robotics industry, underwriting investments for developments that will enable further transformational robotic applications in other contexts.

If you liked this article, you may also be interested in:

• Harvey: A working robot for container crops
• Robots Podcast: FutureDairy
• Air, water, energy and food in a nutshell: Space exploration as driver for sustainable robotic agriculture
• Robotics and the disruptive transformation of agriculture

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‘Drones in agriculture’ has been a topic hot lately, but is it a real business?

As a former Army UAV platoon leader and a current founder of an aerial data collection start-up, I believe so, but when I first got back from Afghanistan, I did not see how drones could be a big thing in agriculture. WHY? I still do not see how the “pilotless app” makes a decisive difference in most civilian applications.  Yet as I expanded my view of a drone to be any flying network node — be it piloted or unpiloted, in the air or in space — I started to see how drone technology could deliver data that creates real value.

Drones may someday perform all kinds of “work” in the field, like spraying or seeding.  However, today the product of the drone industry is not drones, it’s data. Two ideas persuaded me that there is a business here.   The first is that variability information has real value to growers — it is production control data.  The second is that there are places where drone technology allows real data to replace heuristics and estimation in order to drive action.

Agriculture is a very sophisticated business.  Even in specialty crops with less automation, the growers are extremely sophisticated and as scientifically inclined as one would expect of managers of multi-million dollar production facilities.  Putting production facilities out of doors and using production implements derived from nature only increases the complexity of these operations.   In particular, it introduces uncertainty, variation, and risk into the production process.  The managers of these operations, who  have responsibility for maximizing production, are the customers for this data.

When these managers are trying to squeeze the most production out of a given set of inputs, particularly land and labor, there is a natural cycle of input opportunities.  Data only has value if the manager can act on it, and this is where agriculture becomes extremely heterogeneous.  At one end of the spectrum you have dry farming of corn, where there might be three or four viable input opportunities (plant, a spray or two, then harvest), most of which are already done automatically — or robotically — on a combine.  In high value per acre specialty crops (like vegetables, grapes, or nuts), managers might make over 50 discrete, pre-planned input decisions each year, especially if the crop is deficit irrigated.  Even though these crops are in some ways “lower tech” in that they don’t use as much machine automation (such as combines), they do have a much more sophisticated ability to react to variation data.  These are where the drone’s fundamental advantages are most likely to be valued.

The tradeoff in data collection is a tradeoff between the cost of collection per geographic unit and “responsiveness.”  Responsiveness is the degree to which the system can deliver exactly the data needed to make better decisions, exactly when needed, and is a function of many factors including system reliability, resolution, revisit rate, sensor selection, controllability, and ease of deployment.

Drones and other overhead networked systems provide a continuum of data options.  At one end you have LANDSAT satellite data, which provides free, 30-meter pixels, un-interpreted data once every 16 days (if you are lucky enough not have clouds).  At the other end of the continuum, there are hand-launched drones, which have a high real cost per acre because of their short lifespan and the amount of labor required, but can be launched at anytime and are cheap enough to keep in the back of your truck.  In between you have a variety of commercial solutions.

There will always be a tradeoff between cost and responsiveness, but the idea of drones is that electronics will move the efficient frontier in this tradeoff towards more responsiveness at a given cost.  The challenge with this access to better data is not to overshoot the mark.  It is very expensive to overshoot the required responsiveness for the crop.  The trick is match responsiveness to the needs of the manager.  This is the task for the commercial drone industry: match responsiveness to the needs of the decision-maker.

As time goes on, every category of platform across the continuum will have to offer more responsiveness for the same money.  However, the customer responsiveness needs will not increase unless the managers are also given new mechanisms for taking action. Beyond being used to control fast-spreading crop diseases, I do not know what new markets will open up for drones — perhaps they aren’t in agriculture; the film industry certainly requires even more responsiveness than anything in this field  — but it is going to be fun to discover them.

One of the challenges that I would like to lay down to the ground robotics and manipulation communities is to reduce the cost of making an intervention.  Until it makes production sense to go out into a cornfield 20 or 30 times per season and take an action to improve growing, there won’t be a need for more responsive information.  As interventions become more profitable and crop managers decide to increase the number of interventions, there will be a greater need to automate data collection.  Then we can kick off a virtuous cycle of robotic technology.

In mid-2012, four HV-100 robots from Harvest Automation achieved an elusive milestone in robotics: the robots were purchased by a customer and began everyday farm work.  HV-100s, also known as Harvey robots, distribute and collect container-grown plants in greenhouses and on large nursery farms.  Since their introduction, more growers have adopted Harveys, and to date, Harvey robots have moved well over three million plants.

The first crop robots to achieve commercial relevance are now entering service in the nursery and greenhouse sector of agriculture.  Contrary to popular imagination, expert prediction, and much academic research, the first successful agricultural robots are engaged in activities other than fruit and vegetable picking or row crop maintenance.  This article examines the forces that drive the choice of application for commercial robots, describes an early agricultural robot, and suggests areas for further development.

Harvey’s Market

To better understand Harvey, some details of how ornamental plants are grown will be useful.  The plants (classified “specialty crops” by the USDA) for sale at your local garden store begin life under controlled conditions.  Seeds deposited into multi-well trays germinate and, after a few days, the plug containing the sprout is transplanted into a large soil-filled container.  Initially containers sit crowded together, but they soon must be moved apart, or “spaced,” so that the plants can continue to grow without entwining and damaging each other.  Although little known outside the industry, spacing is a critical operation in the life of almost every horticultural plant.  Spacing is one of several operations Harvey robots perform.

A big problem for growers is that spacing is one of the least desirable jobs on the farm.  The task involves bending over, picking up one or more containers in each hand (containers weight up to 22 pounds apiece), walking a few steps, and then bending over again to carefully place them in a specific, predefined pattern. Workers do this task daily throughout the growing season under conditions that range from >100°F heat to cold drizzle.  The work is seasonal, injuries are common, and pay is low.  Labor supply problems are widespread.

The undesirable nature of spacing work results in high turnover; growers find it difficult to predict how many workers will show up on a given day.  Shortages lead to triage.  When too few workers are available, growers perform only the most urgent jobs and neglect others.  Such compromises can affect the yield and quality of the plants and the profitability of the business.

Labor headaches give the nursery and greenhouse industry a strong incentive to adopt robots.  In fact, we have encountered virtually no growers who were unwilling to consider using robots.  The industry offers abundant opportunity for robots: in the US alone, the nursery and greenhouse sector produces about $17 billion worth of plants annually.  An estimated one to two billion container-grown plants are sold every year, and each plant is typically picked up and moved several times during its life at the farm.

Harvey’s Function

In a typical spacing operation plants loaded on a wagon are pulled to an outdoor growing bed.  A multi-tine forklift moves the closely packed plants to the ground en masse (pallets are not used).  From here, the plants must be shifted into a regular pattern that gives them room to grow.

When workers perform the spacing task they often use a grid or other guide aligned with a cord stretched along the edge of the bed.  The guide, marked with tape or paint spots, helps workers position plants accurately in the desired pattern.  The guide is advanced down the bed as workers fill in the pattern.

When robots are used for spacing, a human “robot wrangler” first extends a special robot-detectable tape (called a boundary marker) along one edge of the bed.  The wrangler then places at least one container downfield; this establishes the row where the robot will begin placing plants.  Next the wrangler dials in certain parameters via the robot’s user interface.  Parameters include the spacing pattern (rectangular versus hexagonal), the desired center-to-center distance between plants, and the bed width.  Finally, the wrangler points the robot toward the source of plants and presses the start button.

A planar laser range sensor mounted on the robot measures range every half degree throughout a 180°+ field of view.  Containers appear as semicircles in the range data.  The robot uses this information to locate and approach the nearest accessible container.  The robot grasps the container, picks it up, and then turns in the direction of the boundary marker.  Special boundary sensors scan the ground as the robot moves forward.  When sensors locate the boundary marker, the robot turns to align with and then follow the tape.  Soon the pattern of already-spaced plants comes into view.  The robot locates the next empty position in the pattern, moves to that spot, and deposits the plant it carries.  Afterward, the robot drives back up the bed looking for the next container.  The robot proceeds in this way until there are no more plants to move.

Obstacles (i.e. items that are neither plant containers nor other robots) present a challenge when they appear in the work area.  Robots attempt to respond appropriately by stopping or avoiding the obstacle.  However, current limitations in both sensing and perception leave all robots with less than a human-level appreciation of their situation.  For Harvey this means sometimes reaching for the pant leg of a stationary worker as though it were a container.  Beyond built-in programming protections, worker safety is ultimately assured by Harvey’s relatively small size and weight.  A significant advantage of small robots is that any errors they make or failures they experience become an annoyance rather than a hazard to worker well being.

Harveys can operate in teams.  They do not communicate robot-to-robot but rather recognize each other in the range sensor data.  When one robot encounters a teammate, the following robot waits for the lead robot to get out of the way before continuing with its task.

The technology built into Harvey robots is designed to be the simplest and least expensive that is able to accomplish the required task.  Two items commonly found on outdoor robots that manipulate objects are RTK-GPS for navigation and a six degree-of-freedom arm for object pose control.  Harvey uses neither.  RTK-GPS was excluded in favor of the boundary system for reasons of cost (centimeter-resolution system are expensive), reliability (GPS signal dropouts degrade performance), and user friendliness (the distinctive boundary tape ensures that workers and robots have the same understanding about where plants will be placed).  The simple nature of the manipulation task allows Harvey to perform its job using only an inexpensive, robust, one degree-of-freedom manipulator.

Harvest’s insistence on simplicity, and that every onboard system earns its keep have important consequences.  Development time is minimized and the cost of the robot becomes affordable to growers.  Also, the robots are straightforward enough that even workers with little education can use them effectively.

Harvey’s Rationale

Predictions of the imminent arrival of agricultural robots have been common for perhaps 50 years or more – see for example the Spokane Daily Chronicle’s Robot Farm Machines that Take Over Farm Work (1955), and the Sarasota Herald-Tribune’s Robot Pickers May be a Part of the Future (1983). Robots that pick fruit or vegetables or that weed fields have often been forecast.  But rarely, if ever, has anyone foreseen small robots that would lift and carry potted plants.  Why did Harvest choose such an unanticipated application?

Like any product, a robot striving for commercial success must clear hurdles involving market, technology, and price.  First, the robot must provide a function or service that customers genuinely want.  A robot that fails this test is at best an attractive curiosity.  Second, developers must find a way to construct a reliable robot using existing technology.  Ignoring this constraint can transform a planned development into an interminable research project.  And third, the robot’s price must be competitive with existing solutions to the problem the robot is designed to address.  If the price is too high, potential customers will choose existing solutions and shun the robot.

Robots designed to accomplish long-predicted agricultural applications, say picking oranges or tomatoes, are necessarily more complex than Harvey.  Such robots await the economical solution of interesting research problems in vision, navigation and manipulation.  Harvest’s desire to reach market in a timely manner at a development cost investors were willing to support with a product customers wanted to buy forced us to look beyond commonly predicted applications.  (Some would argue that commercial developers should recoil from any type of robot researchers find interesting.)

Measured against the challenges of market, technology, and price, horticultural plant spacing is very appealing.  The market is large and potential customers are dissatisfied with the status quo.  Fortuitous aspects of the application mean that the technology needed to implement a solution can be relatively simple.

Harvey’s Future

The UN/FAO report How to Feed the World in 2050 (Rome, 2009) estimates that within 37 years world food production must increase by 70% to match demand.  The challenge is that farmers will have access to no more water or arable land than is available today.  The only option is to increase crop yield by making more efficient use of existing resources.

Every autumn at county fairs across the nation, home gardeners and backyard farmers demonstrate one response to this challenge.  By lavishing attention on each plant as it grows, these hobbyists deliver impressive yields of championship produce.  Today’s large farm machinery treats every plant the same but small agricultural robots—by caring for each plant individually—have the potential to make every plant a champion.

Yield and quality are maximized when each plant receives exactly what it requires at the moment of need.  Large numbers of small, inexpensive robots can make this possible.  Robots will constantly monitor soil and plant health.  They can add fertilizer and micro-nutrients as needed, enable pest-resistant/soil enhancing poly cultures, harvest individual fruits and vegetables at the ideal maturity level, prune plants strategically, and eliminate yield-sapping soil compaction.

Eventually the difficult problems blocking development of the long-predicted agricultural robots will be solved.  But the question is how can we avoid waiting for research breakthroughs and instead make progress today?  Harvey offers a hint.

Harvest picked the plant spacing application because it best matched practical market, technology, and cost constraints.  That same filter can be applied to direct the development of the next generation of agricultural robots.  By identifying market-worthy applications that are a small technical step from existing applications we achieve several goals.  Technological risk and time to market are minimized and the cost of the new product becomes predictable.  As an example consider two possible targets for future Harveys: harvesting pumpkins (or perhaps watermelons or cantaloupes) and maintaining food crops grown in containers.

Handling melon-like produce appears near-term because, like the containers Harvey handles now, melons are found on the ground, have a graspable cross-section similar to containers, can be distinguished from vegetation, and are not overly delicate.

Robots that minimize the cost of handling plants grown in containers could cause farmers to switch some food crops from in-ground planting to containers.  Growing crops in containers has several advantages.  Containers allow increased yield by extending the growing season: plants sprouted indoors would move outdoors once the weather warmed.  Another advantage is that, rather than have workers stoop to harvest low-growing fruit in the field, robots could bring plants to workers.

The need is great and the technology – if thoughtfully applied – is available.  The next several years should prove an exciting and productive time for robots in agriculture.

Additional Information

Harvest Automation website:  www.harvestai.com

Hear the word “drone” today and you’ll probably picture some kind of flying weapon, snooping or raining Hellfire missiles from above. But in reality, the first drones you’re likely to see actually in use are more likely to be closer to crop dusters, buzzing over farms. Rather than taking pictures and videos of people, they’ll be surveying fields, using their high-resolution sensors to improve crop yield and decrease agricultural water and chemical use.

Why farms? Because agriculture is a big data problem without the big data .  About half of the “inputs” in farming (from fluids to pesticides, fungicides and herbicides) are typically wasted because they’re applied in greater amount than needed or in the wrong place, such as the ground between plants rather than the plants themselves. That’s considered unavoidable, due to the nature of spray application or the need to avoid under-use of water and chemicals, which can be catastrophic, from disease outbreak to total crop loss.

Soon farmers will know what’s going on with every plant, spotting problems before they spread, and applying chemicals with honeybee precision. They’ll use pesticides and fungicides only when needed and in the smallest amounts necessary, lowering the chemical load in both food and environment and saving money. On a small farm, you can get that level of precision with hand-tending. But on a big farm, the answer is more likely to be robotics, including flying robots — drones.

For the past year, my team at 3D Robotics has been flying our drones on farms, gathering data and talking to farmers about what they want and how they work. These are some of the lessons we’ve learned:

1. Every crop is different! It goes without saying, but grapes are not tomatoes, and tomatoes are not corn. There are hundreds of different kinds of farms, ranging from trees to roots (and that’s not even including livestock and ranching). Each crop needs to be measured differently to generate actionable data. There is no universal crop survey solution, and it will probably be specialists in each particular crop type who ultimately deliver solutions to farmers.
2. Multicopters, not planes. We started with fixed-wing UAVs, but quickly realized that most farms don’t have landing strips. Even short takeoff-and-landing planes get battered fast in regular use without dedicated landing areas, which few farms have. Meanwhile, multicopters, which can take off and land anywhere, are gaining endurance and can now fly for as much as 40 minutes and cover miles. Planes are only suited for the largest farms, and even then missions need to be planned very carefully to find places they can reliably land.
3. Phones/tablets, not laptops. Farmers don’t want to drag laptops into the fields. Any drone that is expected to be used by regular consumers should be entirely operated by a standard Apple or Android smartphone or tablet.
4. One-click auto missions, not “flying”. Likewise, farmers don’t want to have to fly things . Agricultural UAVs should be fully-autonomous, from takeoff to landing. The experience should be as simple as pressing a “Start” button on a phone and the drone flies the entire mission on its own.
5. Fly the camera, not the aircraft: What the farmer is interested in is a picture — not the acquisition of the picture.  Let sophisticated planning tools figure out precisely how to gather the right images, let autonomy take care of the nitty gritty details of flight dynamics, and let humans do what humans do best — specify high-level desires.
6. Video can be worth more than stills. Don’t discount how good farmers are at spotting things with their own eyes. Sometimes a first-person-view live video feed will allow them to spot issues and direct the vehicle to more closely inspect the problem area. (Needless to say, this is only really practical with multicopters). Indeed, farmers may not even know what they’re looking for initially.  Sometimes general situational awareness is the task, rather than delivering a specific data product (such as a mosaic).
7. NDVI is surprisingly easy to do. The gold standard of crop surveying is a “Normalized Differential Vegetation Index”, which shows the difference between regular red light reflected from plants and near-infrared light. Healthy chlorophyll absorbs red and reflects near-IR, while damaged chlorophyll reflect both. It doesn’t take expensive cameras to gather this data. A regular camera slightly modified with a blue bit of plastic becomes a near-IR camera. Take a cheap consumer 3D camera with two lenses, modify one for near-IR, and you’ve got a NDVI camera for less than $200.
8. Aim for crop consultants, not farmers. Most crop data services are provided by local consultants, such as agronomists, not the farmers themselves. At the moment, FAA regulations ban most commercial use of UAVs, defined as anything where money changes hands, so most are used by farmers themselves for their own purposes on their own land. But Congress has mandated that the FAA introduce regulations to allow wider commercial use by 2015 (although it will probably be later than that before this happens). At that point, expect most users to be those local service providers, not the farmers themselves.
9. Time is money. Drones can get answers fast and cheaply, taking advantage of their “anywhere, anytime access to the sky” abilities. That means “timely data on time”, such as daily surveys to find exactly the right time to harvest. Likewise, changes over time can be equally illuminating. The aim of crop surveying is to show the farmers something they can’t see with their own eyes, and the time dimension is a great example of that. By doing regular crop surveys, say every day or week, and using software to highlight differences over time, it’s possible to zero in on growing differences between areas of a field, which may be directly correlated to productivity.
10. Data can be marketing. Some seed companies already offer to do aerial crop surveys for free as part of a sales process, much as they once “walked the field” as part of a free crop analysis process. Similarly, crop survey data can do more than simply guide a farmer into making different crop management decisions. It can also allow the farmer to market their harvest more effectively, pitching such high-tech precision agriculture as a differentiating quality in a commodity field. If data-driven crop management lead farmers to use less chemicals and water, perhaps someday “drone-guided agricultural” will be something consumers could be willing to pay more for.   Done right, big data agriculture means “greener” crops and food.  If consumers will pay a premium for organic, why not for this?

If you liked this article, you may also be interested in:

• Snap 2014: Aerial drones and 3D mapping software for civil engineering surveying
• Down on the farm with drones
• Matching technology to value creation: Drones in agriculture
• UAV innovation is in the Cloud; 3 precision Ag innovations to watch for in 2014

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Link to audio file (38:51)

FutureDairy is an R&D development program to help Australian dairy farmers manage the challenges they are likely to face during the next 20 years. In this episode, Ron Vanderkley speaks with the project lead Kendra Kerrisk from the University of Sydney about robotic milking and herding.

As one of the big challenges is the availability of labor and associated lifestyle issues in the dairy industry, FutureDairy’s focus is on automatic milking systems. While robotic milking technology is now in wide use overseas, there’s less experience with automatic milking in grazing-based farming systems such as in Australia. The video below summarizes some of the results from the DeLaval pilot farm in Australia.

With increasing numbers of Australian dairy cows now being milked by robots, researchers are looking at a range of exciting ways to use robots on the farm. One that has already shown promise is the use of robots (UGV) to herd cattle from the paddock to the dairy. In the videos below you can see two trials with the Shrimp rover hearding cows in Australian farms.

FutureDairy 3 is sponsored by Dairy Australia, the University of Sydney, the NSW Department of Primary Industries and DeLaval.

Kendra Kerrisk
Kendra Kerrisk is Faculty of Veterinary Science at the The University of Sydney in Australia. Kerrisk developed a strong interest for the Dairy Industry whilst conducting undergraduate and post-graduate studies at Massey University in New Zealand and has remained a leader in the field since then. During her PhD at the University of Melbourne she studied Peri-parturient Management for Large Dairy Herds using Controlled Breeding Programmes. After her PhD, she worked for Dexcel (formerly Dairying Research Corporation) in New Zealand on the world’s first pasture-based Automatic Milking System research farm. Through her academic life, she has contributed significantly to the national and international knowledge regarding application of Automatic Milking Systems (AMS) with pasture-based dairying. One of the highlights of the work conducted within FutureDairy, a project which she leads, has been the co-development of the world’s first Robotic Rotary (Automatic Milking Rotary, DeLaval AMRTM). This internationally recognized work will increase the feasibility of robotic milking for large dairy herds that are more common within the Australian and New Zealand industries.

Links:

• Download mp3 (10.9MB)
• Subscribe to Robots using iTunes
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• The FutureDairy Project
• Kendra Kerrisk’s Website

Yet current investment in robotic solutions by the agricultural sector is a fraction of its potential, and many financial, regulatory, practical and social obstacles prevent farmers from implementing these at a commercial scale.

In order to delve into the complex ecosystem of obstacles and pathways to agricultural robotic innovation, Robohub is launching a focus series “Agricultural Robotics” featuring original articles and interviews from leading experts in the fields of robotics, agricultural automation and farming.

The series will look at:

• How and where robotics is currently being used to augment and influence agricultural practice;
• What new innovations are in the R&D pipeline, and what obstacles await these;
• What are the potential business models and pathways for bringing these innovations to the point of commercial viability;
• What farmers and consumers think about incorporating robotics into farming practice; and
• Specific technologies such as UAVs, tractor automation and picking solutions.

We will be covering a whole range of perspectives — R&D development, regulation, business and investment, practical implementation, and sustainable farming — from an excellent line-up of contributors.

Not surprisingly, drone technology features heavily in this series:

• Based on his experience developing drones for farmers, Chris Anderson of 3D Robotics gives us Ten lessons for farm drones
• Ernest Earon of PrecisionHawk argues that, in agriculture, the future of UAV innovation is in the cloud
• Rich Tuttle from AUVSI looks at how the market for field drones, both in the US and internationally, will change when the  FAA lifts its regulations in 2015 in Unmanned systems coming to commercial agriculture
• Robert Morris of TerrAvion looks at drone data and the importance of Matching technology to value creation
• Similarly, Patrick Egan of sUASNews looks at the value proposition of UAVs in Down on the farm with drones

But of course there is more to high-tech farming than drones!

• We will learn about small autonomous farming robots from David Dorhout of Dorhout R&D
• Peter Corke of QUT will discuss the use of robotics for zero-tillage agriculture
• Jorge Heraud of Blue River Technology will talk about using computer vision and robotics to build intelligent solutions for lettuce-thinning
• Kendra Kerrisk of FutureDairy discusses robotic milking and herding technology
• Joseph Jones of Harvest Automation looks at the the forces that drive where commercial robots will be applied in agriculture, using as an example Harvey: A working robot for container crops that distributes and collects container-grown plants in greenhouses and nursery farms
• Jeremy Brown of Jaybridge Robotics explores the trajectory from precision farming to autonomous farming using the Kinze Autonomous Grain Harvesting System as a case study
• Our own John Payne writes about the power of robotics to radically transform the way we think about agriculture
• And Nikolaus Correll from the University of Colorado will take us far beyond earth’s green fields to look at robotic systems for horticulture in outer space

Many thanks to AUVSI for sharing their enlightening infographic on the history of machinery in the fields from their recent issue of Mission Critical. Thanks also to our podcast team, and especially to Ron Vanderkley, who has organized so many of podcast interviews for the series. Thanks Ron!

Hope you enjoy!