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Philipp Lottes is a PhD student at the Photogrammetry Lab at the University of Bonn since November 2015. He received his master’s degree at the Institute of Geodesy and Geoinformation in 2015. During his master studies, he was working as an assistant for the Institute of Geodesy and Geoinformation as well as for the Photogrammetry Lab. Before moving to Bonn, he finished his bachelor studies in Surveying Engineering in 2012 at the Bochum University of Applied Sciences and subsequently worked for the Marx Ingenieurgesellschaft mbH as surveying engineer for 1,5 years. He is currently working as research assistant for the EU funded project FLOURISH. In his research, he focuses on approaches based on machine learning such as (un)supervised learning, transfer learning and deep learning as well as probabilistic techniques in order to develop plant classification systems for agricultural ground robots and unmanned aerial robots.

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.

This paper won the IEEE Robotics & Automation Best Automation Paper Award at ICRA 2017.