LAUSR

Alternanthera philoxeroides, an invasive alien malignant weed, competes with rice for water, fertilizer, light, and growth space before seedling closure stages, which commonly stresses the growth of rice. Chemical herbicides are mainly used to control weeds. However, excessive use of chemical herbicides could lead to serious environmental pollution. With the rapid development of artificial intelligence and deep-learning techniques, it is possible to reduce the use of chemical herbicides by spraying herbicides on only precise regions of weeds in rice fields. To improve the accuracy of the model in identifying weed regions, in this study the performance among the you only look once (YOLO) series and single-shot multibox detector (SSD) one-stage target detection models, i.e., YOLOv3, YOLOv4-tiny, YOLOv5-s, and SSD target detection networks, is discussed. The experimental results showed that the SSD-based target detection model for Alternanthera philoxeroides was better than the YOLO series due to its higher recall, mAP (mean average precision), and F1 values, which reached 0.874, 0.942, and 0.881, respectively. Meanwhile, the target detection model based on SSD performed better than the YOLO series when dealing with mutual occlusion images between seedlings and Alternanthera philoxeroides. In conclusion, in this study a high-accuracy method is provided for detecting precise regions of Alternanthera philoxeroides by constructing a model based on SSD, contributing to the reduction of environmental pollution.