CLR-YOLO: A Lightweight Detection Method for Mechanically Transplanted Rice Seedlings

Accurate identification of plant numbers is crucial for evaluating the effectiveness of mechanical rice seedling transplanting, which directly affects yield estimation and replanting decisions in precision agriculture. Conventional manual counting methods are time-consuming and labor-intensive, which hinders their application in modern agriculture, where efficiency and precision are paramount. Therefore, this study constructed a dataset based on images collected by consumer-grade Unmanned Aerial Vehicles (UAVs) and proposed an improved lightweight detection model named CLR-YOLO (Complex-scene Lightweight Rice-detection YOLO) based on the YOLOv11n. The model replaces the original C3k2 module with C3k2-PConv to improve computational efficiency while maintaining feature extraction capability. Additionally, it reconstructs the neck network using the Heterogeneous Selective Feature Pyramid Network (HSFPN) to optimize the handling of features from both large and small targets. Finally, the PConvHead detection head is designed to enhance feature utilization efficiency and reduce both false positives and missed detections in dense rice seedling scenarios. Experimental results demonstrated that CLR-YOLO achieved an average precision ([email protected]) of 93.9%. While maintaining comparable accuracy, the model reduced parameters to 1.4 M, computational cost to 3.7 GFLOPs, and model size to 2.9 MB—reductions of 46.2%, 41.3%, and 44.2%, respectively, compared to the baseline model. This model provides significant support for rice seedling detection and offers valuable insights to assist agricultural producers in making subsequent decisions.