数字农科院2.0

Lightweight Structure and Attention Fusion for In-Field Crop Pest and Disease Detection

文献类型： 外文期刊

作者： Zijing Luo;Yunsen Liang;Naimin Kong;Lirui Liang;Wenjun Peng;Yujie Yao;Chi Qin;Xiaohan Lu;Mingman Xu;Yining Zhang;Chenyang Lin;Chengyao Jiang;Mengyao Li;Yangxia Zheng;Yameng Jiang;Wei Lu

作者机构：

关键词： agricultural pest and disease detection;attention mechanism;deep learning;lightweight model;mobile deployment;target detection;YOLOv5

期刊名称： Agronomy

ISSN：

年卷期： 2025 年 15 卷 12 期

页码：

收录情况： SCIE（2025版）

摘要： In agricultural production, plant diseases and pests are among the major threats to crop yield and quality. Existing agricultural pest and disease identification methods have problems such as small target scales, complex background environments, and unbalanced sample distributions. This paper proposes a lightweight improved target detection model, YOLOv5s-LiteAttn. Based on YOLOv5s, the model introduces GhostConv and Depthwise Conv to reduce the number of parameters and computational complexity, and it combines CBAM and Coordinate Attention mechanisms to enhance the network’s feature representation capability. Experimental results show that, compared with the basic YOLOv5s model, the number of parameters of the improved model is reduced by 22.75%, and the computational load is reduced by 16.77%. At the same time, mAP@0.5–0.95 is increased by 3.3 percentage points, and recall is improved by 1.1 percentage points. In addition, the inference speed increases from 121 FPS to 142 FPS at an input resolution of 640 × 640, further confirming that the proposed model achieves a favorable trade-off between accuracy and efficiency. The average precision of YOLOv5s-LiteAttn is 97.1%, which outperforms the existing mainstream lightweight detection models. Moreover, an independent test set containing 4328 newly collected field images was established to evaluate generalization and practical applicability. Despite a slight performance decrease compared with the validation results, the model maintained an mAP@0.5–0.95 of 95.8%, significantly outperforming the baseline model, thereby confirming its robustness and cross-domain adaptability. These results confirm that the model has high precision and is lightweight, making it effective for the detection of agricultural diseases and pests.

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