A pruning and knowledge distillation-based YOLOv8 lightweight method for detecting leaf diseases in apple

ZHANG Shuaiping; SHI Lei; ZHENG Guang; WANG Huixin; YIN Fei

doi:10.13300/j.cnki.hnlkxb.2026.03.009

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A pruning and knowledge distillation-based YOLOv8 lightweight method for detecting leaf diseases in apple

Agricultural artificial intelligence | 更新时间：2026-11-05

- A pruning and knowledge distillation-based YOLOv8 lightweight method for detecting leaf diseases in apple
- Journal of Huazhong Agricultural University Vol. 45, Issue 3, Pages: 98-114(2026)
- 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.2026.03.009
  CLC： TP391.4;S661.1
- Received：25 September 2025，
  
  Published：25 May 2026
- 稿件说明：
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张帅平，时雷，郑光，等.基于剪枝和知识蒸馏的YOLOv8轻量化苹果叶片病害检测方法［J］.华中农业大学学报，2026，45（3）：98‐114.

ZHANG Shuaiping,SHI Lei,ZHENG Guang,et al.A pruning and knowledge distillation-based YOLOv8 lightweight method for detecting leaf diseases in apple[J].Journal of Huazhong Agricultural University,2026,45(03):98-114.
张帅平，时雷，郑光，等.基于剪枝和知识蒸馏的YOLOv8轻量化苹果叶片病害检测方法［J］.华中农业大学学报，2026，45（3）：98‐114. DOI： 10.13300/j.cnki.hnlkxb.2026.03.009.

ZHANG Shuaiping,SHI Lei,ZHENG Guang,et al.A pruning and knowledge distillation-based YOLOv8 lightweight method for detecting leaf diseases in apple[J].Journal of Huazhong Agricultural University,2026,45(03):98-114. DOI： 10.13300/j.cnki.hnlkxb.2026.03.009.

摘要

为解决现有苹果叶片检测方法在准确率、实时性及复杂噪声环境下鲁棒性等方面的问题，基于YOLOv8n提出一种轻量化、实时苹果叶片病害检测模型SMPD-YOLO。首先，将SPPFCSPC（spatial pyramid pooling fast cross stage partial CSP）特征金字塔模块嵌入到主干网络，增强特征融合能力。其次，引入MPD-IoU（minimum point distance-IoU）作为边界框回归损失函数，提高模型的精度和收敛速度。再次，通过基于层自适应幅度剪枝（layer adaptive magnitude-based pruning，LAMP）进一步压缩模型体积、降低浮点运算速度。最后，采用通道级知识蒸馏（channel-wise knowledge distillation，CWD）策略，提升检测性能。实验结果显示，改进后SMPD-YOLO模型在IoU（intersection over union）阈值大于0.5时的平均精度均值和帧率分别达到90.20%和133.3帧/s，模型权重、浮点运算速度分别为5.0 MB、7.3×10

-1

。此外，改进模型在强光、弱光及图像模糊复杂噪声环境下仍能展现出优异的鲁棒性。结果表明，SMPD-YOLO模型兼具高准确性、轻量化和实时性，可在资源受限设备上实现高效叶片病害检测。

Abstract

A lightweight， real-time model of detecting leaf diseases in apple， named as SMPD-YOLO， was proposed based on YOLOv8n to solve the problems in existing methods of detecting leaf diseases in apple in terms of accuracy， real-time performance， and robustness under complex and noisy environments. The spatial pyramid pooling fast cross stage partial CSP （SPPFCSPC） module was embedded into the backbone network t

o enhance the capability of feature fusion. The minimum point distance-IoU （MPD-IoU） was introduced as the bounding box regression loss function to improve the accuracy and convergence speed of the model. The model volume and floating point operations were further reduced via layer adaptive magnitude‑based pruning （LAMP）. A channel-wise knowledge distillation （CWD） strategy was used to boost detection performance. The results showed that the improved SMPD-YOLO model had a mean average precision（mAP@0.5） of 90.20% and a frame rate of 133.3 frames per second （FPS），while the weight and FLOPs of model was 5.0 MB and 7.3×10

-1

， respectively. The improved model maintained excellent robustness under complex and noisy environments including strong illumination， weak light， and blurred images. It is indicated that the SMPD-YOLO model combines high accuracy， lightweight design， and real-time performance， enabling the efficient detection of leaf diseases on resource-constrained equipment.

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Keywords

references

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