CLM-YOLO: An Improved YOLOv11n-Based Model for Accurate Rice Pest Detection and Intelligent Monitoring

Rice is one of the most important food crops in China, and frequent outbreaks of rice pests pose a serious threat to both the yield and quality of rice. Accurate monitoring of rice pests is therefore of great significance for field management and intelligent pest control. In this study, a refined rice pest detector, termed CLM-YOLO, is proposed based on YOLOv11n according to the characteristics of rice pest targets. Specifically, in the backbone network, the original C3k component in the C3k2 block is replaced with an improved RepViT-DBlock to form the C3k2R module, which strengthens early-stage local feature representation and improves the extraction of fine-grained pest-related cues. Additionally, the Multi-Dimensional Grouped Convolutional Block Attention Module (MDGCBAM) is embedded at the transition between the backbone and neck, allowing the network to emphasize pest-related regions while suppressing redundant responses from rice-field backgrounds. Finally, the Local Deformable Attention Adaptive Query Upsampling (LDAAQU) module is adopted in the neck to replace the original upsampling operation. Through deformable attention and query-guided adaptive aggregation, LDAAQU improves the spatial alignment of multi-scale features during feature fusion and enhances the recovery of fine-grained image details. Experimental results show that CLM-YOLO achieves favorable performance on key detection metrics. Specifically, the F1-score, mAP@0.5, and mAP@0.5:0.95 reached 84.37%, 87.8%, and 73.1%, respectively. The proposed method offers a feasible approach for accurate rice pest detection and intelligent pest monitoring in field environments.