EMD-YOLO： an improved lightweight algorithm for detecting diseases in crop leaf based on YOLOv8n

WANG Binbing; ZHANG Yali; ZHENG Guang; SHI Lei; YIN Fei

doi:10.13300/j.cnki.hnlkxb.2025.04.018

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EMD-YOLO： an improved lightweight algorithm for detecting diseases in crop leaf based on YOLOv8n

Plant protection | 更新时间：2026-11-05

- EMD-YOLO： an improved lightweight algorithm for detecting diseases in crop leaf based on YOLOv8n
- Journal of Huazhong Agricultural University Vol. 44, Issue 4, Pages: 181-191(2025)
- 作者机构：
  
  河南农业大学信息与管理科学学院，郑州 450046
- 作者简介：
- 基金信息：
- DOI：10.13300/j.cnki.hnlkxb.2025.04.018
  CLC： S432;TP391.41
- Received：28 November 2024，
  
  Published：05 July 2025
- 稿件说明：
移动端阅览
王斌兵，张亚利，郑光，等.EMD-YOLO：基于YOLOv8n改进的轻量化作物叶部病害检测算法［J］.华中农业大学学报，2025，44（4）：181‐191.

WANG Binbing,ZHANG Yali,ZHENG Guang,et al.EMD-YOLO： an improved lightweight algorithm for detecting diseases in crop leaf based on YOLOv8n[J].Journal of Huazhong Agricultural University,2025,44(04):181-191.
王斌兵，张亚利，郑光，等.EMD-YOLO：基于YOLOv8n改进的轻量化作物叶部病害检测算法［J］.华中农业大学学报，2025，44（4）：181‐191. DOI： 10.13300/j.cnki.hnlkxb.2025.04.018.

WANG Binbing,ZHANG Yali,ZHENG Guang,et al.EMD-YOLO： an improved lightweight algorithm for detecting diseases in crop leaf based on YOLOv8n[J].Journal of Huazhong Agricultural University,2025,44(04):181-191. DOI： 10.13300/j.cnki.hnlkxb.2025.04.018.

摘要

为准确、快速地识别作物叶部病害，降低手工诊断成本，减少叶部病害对作物生长过程与产量的危害，提出一种基于YOLOv8n新型轻量级作物叶部病害检测算法EMD-YOLO。该算法结合多尺度空洞注意力MSDA、EffectiveSE注意力机制、DySample上采样以及Wise-IoU损失函数。其中，多尺度空洞注意力MSDA结合多尺度空间卷积与注意力机制，提高多尺度特征提取效率；EffectiveSE强化特征选择，提升模型表示性能；DySample上采样保留重要特征，提高特征图分辨率和检测性能；Wise-IoU损失函数优化交并比（IoU）计算方式，提升模型定位精度。结果显示，EMD-YOLO的精确度、mAP@0.5、模型权重分别为96.3%、92.8%、4.85 MB，较基线模型YOLOv8n的精确度和平均精度均值分别提高3.0和3.6百分点，权重降低1.4 MB。结果表明，EMD-YOLO的泛化性良好，适用于移动端农作物叶部病害检测设备。

Abstract

A novel lightweight algorithm for detecting diseases in crop leaf based on YOLOv8n，EMD-YOLO was proposed to accurately and rapidly identify diseases，reduce costs of manual diagnosis，and minimize the impact of leaf diseases on the production and quality of crops.The algorithm integrated the multi-scale dilated attention （MSDA），EffectiveSE attention mechanism，DySample upsampling，and Wise-IoU loss function.MSDA was combined with multi-scale spatial convolution and attention mechanisms to increase the efficiency of extracting multi-scale feature，while EffectiveSE strengthened the selection of feature and improved the representation performance of model.DySample upsampling preserved important features to enhance the map resolution of feature and the detection performance.Wise-IoU loss function optimized intersection over union （IoU） computation to improve the localization accuracy of model.The results showed that the accuracy，mAP@0.5，and weight of EMD-YOLO was 96.3%，92.8%，and 4.85 MB，respectively.The accuracy and mAP@0.5 of YOLOv8n increased by 3.0 and 3.6 percentage points compared with that of the baseline model YOLOv8n，respectively，while the weight decreased by 1.4 MB.It is indicated that EMD-YOLO has good generalization and is suitable for detecting diseases in crop leaf with mobile devices.

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references

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