In episode four of season three Neil introduces us to the ideas behind the bias variance dilemma (and how how we can think about it in our daily lives). Plus, we answer a listener question about how to make sure your neural networks don’t get fooled. Our guest for this episode is Jeff Dean, Google Senior Fellow in the Research Group, where he leads the Google Brain project. We talk about a closet full of robot arms (the arm farm!), image recognition for diabetic retinopathy, and equality in data and the community.
Crops are key for a sustainable food production and we face several challenges in crop production. First, we need to feed a growing world population. Second, our society demands high-quality foods. Third, we have to reduce the amount agrochemicals that we apply to our fields as it directly affects our ecosystem. Precision farming techniques offer a great potential to address these challenges, but we have to acquire and provide the relevant information about the field status to the farmers such that specific actions can be taken.
The market for agricultural robots has the opportunity for significant expansion: the farming world needs to increase global production whilst it also faces challenges such as reduced availability and the rising costs of farm labour.
CBS News profiled a New Jersey vertical farm providing baby kale, arugula, spinach and romaine to nearby Newark and NYC groceries. They boast 130 times more productivity, 95% less water and no pesticides versus field farms. And they harvest 24 times a year, rain, snow or shine.
Let’s assume, for a moment, that the vision I’ve laid out in this blog is ridiculously successful, and, over the next few decades, robotic devices take over all aspects of tending land and crops and handling material inputs and produce, and do it using increasingly sustainable practices that begin the process of retaining and enhancing biological diversity and reviving overworked soils. What’s left for farmers to do? Will there even be a need for humans on farms?
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.
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.
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.