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The CRRE-YOLO model, an enhanced YOLOv11 framework incorporating efficient local attention and multiscale convolution, outperforms existing models like YOLOv11, YOLOv8, and RT-DETR in rice pest detection accuracy while maintaining low computational requirements. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

December 29, 2025Applied Sciences1 citationsOpen Access

CRRE-YOLO: An Enhanced YOLOv11 Model with Efficient Local Attention and Multiscale Convolution for Rice Pest Detection

GZGuangzhuo ZhangZhejiang Ocean University YRYandong RuZhejiang Ocean University

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Abstract

Accurate and real-time detection of rice pests is crucial for protecting crop yield and advancing precision agriculture. However, existing models often suffer from limitations in small-object recognition, background interference, and computational efficiency. To overcome these challenges, this study proposes an improved lightweight detection framework, CRRE-YOLO, developed based on YOLOv11. The model integrates four enhanced components—the EIoU loss function, C2PSAELA module, RPAPAttention mechanism, and RIMSCConv module—to improve localization accuracy, feature extraction, and fine-grained pest recognition. Experimental results on the RP11-Augmented dataset show that CRRE-YOLO achieves 0. 852 precision, 0. 787 recall, 83. 6% mAP@0. 5, and 71. 9% mAP@0. 5: 0. 95, outperforming YOLOv11 by up to 7. 8% and surpassing YOLOv8 and RT-DETR in accuracy while maintaining only 2. 344M parameters and 6. 1G FLOPs. These results demonstrate that CRRE-YOLO achieves an optimal balance between accuracy and efficiency, providing a practical and deployable solution for real-time rice pest detection and offering potential for integration into smart farming and edge computing applications.

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