LAUSR

BACKGROUND Precision weed control in vegetable fields can substantially reduce the required weed control inputs. Rapid and accurate weed detection in vegetable fields is a challenging task due to the presence of a wide variety of weed species at various growth stages and densities. This paper presents a novel deep learning-based method for weed detection that recognizes vegetable crops and classifies all other green objects as weeds. RESULTS The optimal confidence threshold values for YOLO-v3, CenterNet, and Faster R-CNN were 0.4, 0.6, and 0.4/0.5, respectively. These deep learning models had the average precision (AP) above 97% in the testing dataset. YOLO-v3 was the most accurate model for detection of vegetables and yielded the highest F1 score of 0.971, along with high precision and recall values of 0.971 and 0.970, respectively. The inference time of YOLO-v3 was similar to CenterNet, but significantly shorter than that of Faster R-CNN. Overall, YOLO-v3 showed the highest accuracy and computational efficiency among the deep learning architectures evaluated in this study. CONCLUSION These results demonstrate that deep learning-based methods can reliably detect weeds in vegetable crops. The proposed method avoids dealing with various weed species, and thus greatly reduces the overall complexity of weed detection in vegetable fields. Findings have implications for advancing site-specific robotic weed control in vegetable crops.