Cognitive Weeding: An Approach to Single-Plant Specific Weed Regulation

Mark Niemeyer, Marian Renz, Maren Pukrop, David Hagemann, Tim Zurheide, Daniel Di Marco, Markus Höferlin, Philipp Stark, Florian Rahe, Matthias Igelbrink, Mario Jenz, Thomas Jarmer, Dieter Trautz, Stefan Stiene, Joachim Hertzberg

Abstract: This paper provides a comprehensive overview of the architecture required to implement selective weeding in arable farming, as developed within the Cognitive Weeding project. This end-to-end architecture begins with data acquisition utilizing drones, robots, or agricultural machinery, followed by data management, AI-based data annotation, knowledge-based inference to determine the necessary treatment, resulting in an application map for selective hoeing. The paper meticulously details the various components of the architecture and illustrates through examples how they are interconnected.