Robotic weed control using automated weed and crop classification Xiaolong Wu, Stéphanie Aravecchia, Philipp Lottes, Cyrill Stachniss, Cédric Pradalier To cite this version: Xiaolong Wu, Stéphanie Aravecchia, Philipp Lottes, Cyrill Stachniss, Cédric Pradalier. Robotic weed control using automated weed and crop classification. 2020. hal-02484462 HAL Id: hal-02484462 https://hal.archives-ouvertes.fr/hal-02484462 Preprint submitted on 19 Feb 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Robotic Weed Control Using Automated Weed and Crop Classification Xiaolong Wu Stephanie´ Aravecchia Georgia Institute of Technology UMI2958 GeorgiaTech-CNRS Department of Electrical and Computer Engineering Metz 57070, France Atlanta, GA 30332, United States
[email protected] [email protected] Philipp Lottes Cyrill Stachniss University of Bonn University of Bonn Department of Photogrammetry Department of Photogrammetry Bonn 53115, Germany Bonn 53115, Germany
[email protected] [email protected] Cedric´ Pradalier UMI2958 GeorgiaTech-CNRS Metz 57070, France
[email protected] Abstract Autonomous robotic weeding systems in precision farming have demonstrated their full potential to alleviate the current dependency on agrochemicals such as herbicides and pesticides, thus reducing environmental pollution and improving sustainability.