Cotton leaf disease detection based on EDW-YOLOv8

LI Ya; JIANG Chen; WANG Hairui; ZHU Guifu; HU Can

doi:10.13300/j.cnki.hnlkxb.2025.05.019

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Cotton leaf disease detection based on EDW-YOLOv8

Plant protection | 更新时间：2026-11-05

- Cotton leaf disease detection based on EDW-YOLOv8
- Journal of Huazhong Agricultural University Vol. 44, Issue 5, Pages: 189-197(2025)
- 作者机构：
  
  1.昆明理工大学信息工程与自动化学院，昆明 650504
  2.昆明理工大学信息化建设管理中心/昆明理工大学⁃曙光信息产业股份有限公司AI联合研究中心，昆明 650504
- 作者简介：
- 基金信息：
- DOI：10.13300/j.cnki.hnlkxb.2025.05.019
  CLC： TP391.4;S24
- Received：21 May 2024，
  
  Published：15 September 2025
- 稿件说明：
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李亚，蒋晨，王海瑞，等.基于EDW-YOLOv8的棉花叶片病害检测［J］.华中农业大学学报，2025，44（5）：189‐197.

LI Ya,JIANG Chen,WANG Hairui,et al.Cotton leaf disease detection based on EDW-YOLOv8[J].Journal of Huazhong Agricultural University,2025,44(05):189-197.
李亚，蒋晨，王海瑞，等.基于EDW-YOLOv8的棉花叶片病害检测［J］.华中农业大学学报，2025，44（5）：189‐197. DOI： 10.13300/j.cnki.hnlkxb.2025.05.019.

LI Ya,JIANG Chen,WANG Hairui,et al.Cotton leaf disease detection based on EDW-YOLOv8[J].Journal of Huazhong Agricultural University,2025,44(05):189-197. DOI： 10.13300/j.cnki.hnlkxb.2025.05.019.

摘要

为解决复杂自然环境背景下棉花叶片病害检测准确率低的问题，提出一种基于改进YOLOv8n的棉花叶片病害检测模型。首先在YOLOv8n的骨干网络处加入EMA注意力机制，同时在骨干网络中的C2f模块中加入可变形卷积Deformable ConvNets v2模块，扩大感受野以加强特征提取能力。在此基础上，将损失函数CIoU替换为具有动态聚焦机制的边界框回归损失WIoU，以加快模型收敛速度，进一步提升模型性能。试验结果显示，改进后的EDW-YOLOv8模型准确率、召回率和平均精度相较于YOLOv8n分别提升了4.3、7.5和4.6百分点。结果表明，研究所提出的模型具有良好的泛化性，可以准确高效地检测出图像中棉花叶片病害目标。

Abstract

Accurate detection of cotton leaf diseases in complex natural environments is essential for effectively minimizing the impact of diseases on cotton yield and quality.To address this，this study proposes a cotton leaf disease detection model based on an improved YOLOv8n.First，the EMA attention mechanism is integrated into the backbone network of YOLOv8n.Simultaneously，the deformable convolutional module，Deformable ConvNets v2，is incorporated into the C2f module within the backbone network to expand the sensory field and strengthen feature extraction capabilities.Additionally，the CIoU loss function is replaced with the WIoU bounding box regression loss，which includes a dynamic focusing mechanism to accelerate model convergence and further improve performance.The experimental results showed that the improved EDW-YOLOv8 model achieves increases of 4.3%，7.5% and 4.6% in accuracy，recall，and average precision，respectively，compared with the original YOLOv8n.These results show that the proposed model，with good generalization ability，can accurately and efficiently detect cotton leaf disease targets in images.

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references

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