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

Abstract

Introduction The number of rice leaves largely reflects the growth stage and health status of rice. However, the current rice leaf counting method is time-consuming and laborious, with low accuracy and poor efficiency, which is difficult to meet the needs of rice growth monitoring. Methods This study proposes a field rice leaf detection method based on an improved YOLOv5s model. First, we added a high-resolution layer and removed the original low-resolution detection layer, using the K-Means++ clustering algorithm to reset the anchor box sizes, enhancing the model's ability to identify small leaf tip targets while reducing the number of parameters. Second, we introduced a coordinate attention mechanism (CA) to strengthen focus on leaf tip features under weed interference and leaf occlusion conditions. Finally, we employed a content-aware reassembly of feature (CARAFE) upsampling operator to enhance the detail reconstruction capability of leaf tip features. Results and discussion 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.

Code and data availability

The paper describes a 1,200-image field rice leaf-tip dataset (expanded to 3,600 images, 141,411 labels) and an improved YOLOv5s model, but no public repository, code, or dataset URL is provided. The data availability statement only promises raw data from the authors on request, so the dataset/model are paper-specific但

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