A comparative applied analysis of six robotic-assisted weeding systems in sugar beets

Sonja I. Kimmel, Matthias Schumacher, Michael Spaeth, Markus Sökefeld, Oyebanji O. Alagbo, Alicia Allmendinger, Dionisio Andujar, Therese W. Berge, Reiner Braun, Sergiu Cioca Parasca, Jessica Emminghaus, Ioannis Glykos, Pavel Hamouz, Adam Hruška, Michael Merkle, Georg Naruhn, Gerassimos G. Peteinatos, Bahadir Sin and Roland Gerhards
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Sonja I. Kimmel: Weed Science Department, Faculty of Agricultural Science, University of Hohenheim, Stuttgart, Germany
Matthias Schumacher: Weed Science Department, Faculty of Agricultural Science, University of Hohenheim, Stuttgart, Germany
Michael Spaeth: Weed Science Department, Faculty of Agricultural Science, University of Hohenheim, Stuttgart, Germany
Markus Sökefeld: Weed Science Department, Faculty of Agricultural Science, University of Hohenheim, Stuttgart, Germany
Oyebanji O. Alagbo: Department of Crop Production and Protection, Faculty of Agriculture, Obafemi Awolowo University, Ile-Ife, Nigeria
Alicia Allmendinger: Weed Science Department, Faculty of Agricultural Science, University of Hohenheim, Stuttgart, Germany
Dionisio Andujar: CSIC - Centre for Automation and Robotics, Madrid, Spain
Therese W. Berge: Department of Invertebrate Pests and Weeds in Forestry, Agriculture and Horticulture, Norwegian Institute of Bioeconomy Research (NIBIO), Ås, Norway
Reiner Braun: Reutlingen University, Herman Hollerith Centre (HHZ), Reutlingen, Germany
Sergiu Cioca Parasca: USAMV - University of Agricultural Sciences and Veterinary Medicine, Cluj-Napoca, Romania
Jessica Emminghaus: Weed Science Department, Faculty of Agricultural Science, University of Hohenheim, Stuttgart, Germany
Ioannis Glykos: Department of Natural Resources and Agricultural Engineering, Agricultural University of Athens, Greece; Soil and Water Resources Institute, Hellenic Agricultural Organisation - DIMITRA, Athens, Greece
Pavel Hamouz: CULSP, Czech University of Life Sciences Prague, Prague, Czech Republic
Adam Hruška: CULSP, Czech University of Life Sciences Prague, Prague, Czech Republic
Michael Merkle: Weed Science Department, Faculty of Agricultural Science, University of Hohenheim, Stuttgart, Germany
Georg Naruhn: Weed Science Department, Faculty of Agricultural Science, University of Hohenheim, Stuttgart, Germany
Gerassimos G. Peteinatos: Department of Natural Resources and Agricultural Engineering, Agricultural University of Athens, Greece; Soil and Water Resources Institute, Hellenic Agricultural Organisation - DIMITRA, Athens, Greece
Bahadir Sin: Sakarya University of Applied Science, Sakarya, Turkey
Roland Gerhards: Weed Science Department, Faculty of Agricultural Science, University of Hohenheim, Stuttgart, Germany

Plant, Soil and Environment, 2025, vol. 71, issue 11, 782-792

Abstract: Effective weed management is crucial in the critical period of sugar beet production, but often lacks sustainability and environmental protection. Recent advancements in sensor-based weed control systems have rendered the latter a realistic prospect, which demands detailed analyses, especially under suboptimal field conditions. The present study analysed six robotic-assisted weed control systems (RAWS) in three experiments on sugar beets in 2024, conducted under dry soil and high weed pressure. The experiments included sensor-based inter-row and intra-row hoeing, spot- and band-spraying and were compared to a broadcast herbicide treatment and an untreated control. Weed control efficacy (WCE) in the intra- and inter-row areas, as well as weed species composition and crop plant damage, were assessed after treatment. The data show that intra-row WCE of two hoeing robots (Farming GT® and Robovator®) equipped with selective intra-row blades achieved up to 80%, which was higher than the broadcast herbicide control with 67% WCE. In the inter-row area, Farming GT® robotic hoeing and ARA® spot-spraying resulted in more than 90% WCE, which was equal to the broadcast herbicide application. Weed species composition was not affected by the different RAWS. Crop plants were affected by all hoeing treatments with maximum non-lethal burial rates of 33%. The highest lethal uprooting of crop plants occurred after Farming GT® robotic hoeing, at 5.5% overall. The results demonstrate the great potential of robotic weeding to replace broadcast herbicide applications.

Keywords: weeding robots; plant detection; sensor technologies; artificial intelligence; precision farming (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:caa:jnlpse:v:71:y:2025:i:11:id:335-2025-pse

DOI: 10.17221/335/2025-PSE

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