An improved YOLOv5s-based method for detecting rice leaves in the field

Experimental results showed that the improved rice leaf tip detection model achieved precision, recall, and mean average precision rates of 93.7%, 87%, and 93.5%, respectively, with a parameter count of 5.02 million (M), improving by 6.5%, 22.1%, and 18.5% compared to the YOLOv5s baseline model, while reducing the parameter count by 28.4%. The improved model effectively reduced the missed detection rate of rice leaves and enhanced the accuracy and robustness of field rice leaf tip detection, providing strong technical support for rice phenotype feature extraction and growth monitoring.