An Enhanced and Lightweight YOLOv8-Based Model for Accurate Rice Pest Detection

Accurate pest identification is crucial for ensuring both high quality and high yield in rice production. This paper proposes RicePest-YOLO, a practical and generalizable model designed for agricultural pest detection, based on structural optimization and lightweight strategies applied to the YOLOv8 architecture. The model is specifically tailored to address the challenges of rice pest images, such as small insect size, complex backgrounds, and high inter-class similarity. In particular, it integrates ODConv and the BiFPN structure into the backbone and neck of YOLOv8n, enhancing the model’s ability to extract features from rice pest images. Subsequently, the Shape-IoU loss function is employed to enhance the model’s sensitivity and precision in target recognition. Furthermore, to facilitate the deployment of the new algorithm on embedded terminal devices, we applied Layer Adaptive Magnitude Pruning and Knowledge Distillation techniques for a lightweight model. The effectiveness of the proposed model was evaluated on common rice pest datasets. Experimental results demonstrate that the proposed model outperforms YOLOv8n, achieving an mAP@0.5 of 94.3%, and an mAP@0.5:0.95 of 67.3%, with 2.7% improvement in mAP@0.5 and 1.2% improvement in mAP@0.5:0.95. Comparison experiments with state-of-the-art models further demonstrated the proposed approach’s superiority. The model’s generalization and robustness were tested on the Pests dataset and the Paddy Rice Pest dataset. Results indicated a 2.6% and 3.0% improvement in mAP@0.5, and a 2.8% and 4.0% increase in mAP@0.5:0.95, respectively. Additionally, Experimental results show that the lightweight version of RicePest-YOLO reduced parameters by 48.1% and GFLOPs by 50%, with only a marginal decrease of 0.4% in mAP@0.5. The results validate that the proposed model is not only effective and efficient but also robust and lightweight, making it a promising solution for rice pest detection and offering valuable support for pest management in precision agriculture.