Assessment of the Performance of a Field Weeding Location-Based Robot Using YOLOv8

Abstract

Field robots are an important tool when improving the efficiency and decreasing the climatic impact of food production. Although several commercial field robots are available, the advantages, limitations, and optimal utilization methods of this technology are still not well understood due to its novelty. This study aims to evaluate the performance of a commercial field robot for seeding and weeding tasks. The evaluation was carried out in a 2-hectare sugar beet field. The robot’s performance was assessed by counting plants and weeds using image processing. The YOLOv8 model was trained to detect sugar beets and weeds. The plant and weed densities were compared on a robotically weeded area of the field, a chemically weeded control area, and an untreated control area. The average weed density on the robotically treated area was about two times lower than that on the untreated area and about three times higher than on the chemically treated area. The testing robot in the specific testing environment and mode showed intermediate results, weeding a majority of the weeds between the rows; however, it left the most harmful weeds close to the plants. Software for robot performance assessment can be used for monitoring robot performance and plant conditions several times during plant growth according to the weeding frequency.

Plant phenotyping relevance

YOLOv8画像処理で作物・雑草を検出し、植物密度を定量化する手法とロボット性能評価ソフトが研究の中心であり、植物状態の反復モニタリングに用いるため。

Code and data availability

The paper's field image dataset (2272 sugar beet/weed images) is openly available on Zenodo, and the authors' Matlab robot-performance analysis software is publicly hosted on GitHub. Both are paper-specific, public, and actionable.