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Auflistung nach Autor:in "Brandt, David"

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  • Zeitschriftenartikel
    Weed Management of the Future
    (KI - Künstliche Intelligenz: Vol. 33, No. 4, 2019) Amend, Sandra; Brandt, David; Di Marco, Daniel; Dipper, Tobias; Gässler, Gabriel; Höferlin, Markus; Gohlke, Maurice; Kesenheimer, Katharina; Lindner, Peter; Leidenfrost, Roland; Michaels, Andreas; Mugele, Tobias; Müller, Arthur; Riffel, Tanja; Sampangi, Yeshwanth; Winkler, Jan
    The methods used to protect agricultural products currently undergo drastic changes. Artificial Intelligence is a prime candidate to overcome two challenges faced by farmers around the world: The increasing cost and decreasing availability of human labor for weed control, and the growing global restriction of herbicides. Deep Learning is one of the most prominent approaches for applying AI to all kinds of use cases in industrial applications, entertainment, and security. Its latest field of application is plant classification that enables automated weed control and precise spot spraying of herbicides. While cheap, powerful platforms for deploying classification mechanisms are widely available, this comes at the cost of expensive and effort rich classifier training. This effectively makes Deep Learning-based approaches unavailable for the majority of the agricultural sector. Deepfield Robotics presents a systematic approach for deploying AI onto fields at large, including the learnings that led to their self-contained AI driven plant classification modules that relieve individuals from having to deploy their own AI solution. The same technology acts as enabler for more agricultural domains, such as targeted fertilization, nano irrigation, and automated phenotyping. This article documents Deepfield Robotics’ findings and vision on how AI can be the workhorse for agricultural weeding labor.