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...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11003895/ |
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| author | Guisuo Liu Juxing Di Qing Wang Yan Zhao Yang Yang |
| author_facet | Guisuo Liu Juxing Di Qing Wang Yan Zhao Yang Yang |
| author_sort | Guisuo Liu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8736da3d531f439b85a0fef7dd1e3ad1 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8736da3d531f439b85a0fef7dd1e3ad12025-08-20T03:12:36ZengIEEEIEEE Access2169-35362025-01-0113910469106410.1109/ACCESS.2025.356981911003895An Enhanced and Lightweight YOLOv8-Based Model for Accurate Rice Pest DetectionGuisuo Liu0Juxing Di1https://orcid.org/0009-0008-8120-0595Qing Wang2https://orcid.org/0009-0002-1765-1769Yan Zhao3https://orcid.org/0009-0000-1862-1057Yang Yang4https://orcid.org/0009-0001-4910-9387College of Information Engineering, Hebei University of Architecture, Zhangjiakou, ChinaCollege of Information Engineering, Hebei University of Architecture, Zhangjiakou, ChinaCollege of Information Engineering, Hebei University of Architecture, Zhangjiakou, ChinaCollege of Artificial Intelligence, Beijing Normal University, Beijing, ChinaCollege of Information Engineering, Hebei University of Architecture, Zhangjiakou, ChinaAccurate 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.https://ieeexplore.ieee.org/document/11003895/Rice pest detectionfeature extractionYOLOV8nloss functionmodel compression |
| spellingShingle | Guisuo Liu Juxing Di Qing Wang Yan Zhao Yang Yang An Enhanced and Lightweight YOLOv8-Based Model for Accurate Rice Pest Detection IEEE Access Rice pest detection feature extraction YOLOV8n loss function model compression |
| title | An Enhanced and Lightweight YOLOv8-Based Model for Accurate Rice Pest Detection |
| title_full | An Enhanced and Lightweight YOLOv8-Based Model for Accurate Rice Pest Detection |
| title_fullStr | An Enhanced and Lightweight YOLOv8-Based Model for Accurate Rice Pest Detection |
| title_full_unstemmed | An Enhanced and Lightweight YOLOv8-Based Model for Accurate Rice Pest Detection |
| title_short | An Enhanced and Lightweight YOLOv8-Based Model for Accurate Rice Pest Detection |
| title_sort | enhanced and lightweight yolov8 based model for accurate rice pest detection |
| topic | Rice pest detection feature extraction YOLOV8n loss function model compression |
| url | https://ieeexplore.ieee.org/document/11003895/ |
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