Improved CycleGAN-based method for augmenting images of few-shot maize diseases

LI Yanling; ZHANG Boxiang; LI Feitao; BACAO Fernando; SI Haiping; CHEN Lina

doi:10.13300/j.cnki.hnlkxb.2025.05.020

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Improved CycleGAN-based method for augmenting images of few-shot maize diseases

Plant protection | 更新时间：2026-11-05

- Improved CycleGAN-based method for augmenting images of few-shot maize diseases
- Journal of Huazhong Agricultural University Vol. 44, Issue 5, Pages: 198-207(2025)
- 作者机构：
  
  1.河南农业大学信息与管理科学学院，郑州 450002
  2.新里斯本大学信息管理学院，里斯本 1070⁃312
  3.商丘师范学院计算机与信息技术学院，商丘 476000
- 作者简介：
- 基金信息：
- DOI：10.13300/j.cnki.hnlkxb.2025.05.020
  CLC： S513;TP391.1
- Received：22 March 2025，
  
  Published：15 September 2025
- 稿件说明：
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李艳玲，张博翔，李飞涛，等.基于改进CycleGAN的小样本玉米病害图像扩充方法［J］.华中农业大学学报，2025，44（5）：198‐207.

LI Yanling,ZHANG Boxiang,LI Feitao,et al.Improved CycleGAN-based method for augmenting images of few-shot maize diseases[J].Journal of Huazhong Agricultural University,2025,44(05):198-207.
李艳玲，张博翔，李飞涛，等.基于改进CycleGAN的小样本玉米病害图像扩充方法［J］.华中农业大学学报，2025，44（5）：198‐207. DOI： 10.13300/j.cnki.hnlkxb.2025.05.020.

LI Yanling,ZHANG Boxiang,LI Feitao,et al.Improved CycleGAN-based method for augmenting images of few-shot maize diseases[J].Journal of Huazhong Agricultural University,2025,44(05):198-207. DOI： 10.13300/j.cnki.hnlkxb.2025.05.020.

摘要

针对玉米病害图像识别任务存在数据集获取困难、样本不足及不同类别病害样本不均衡等问题，设计一种基于改进CycleGAN（cycle-consistent adversarial networks）的图像数据增强方法。首先，使用较小感受野的卷积核优化CycleGAN网络结构，生成高质量样本图像，减少过拟合现象发生；其次，将SE（squeeze-excitation）注意力机制嵌入到生成器的残差模块中，增强CycleGAN对病害特征的提取能力，使网络更准确地捕捉小目标病害或域间差异不明显的特征。结果显示，改进后的CycleGAN相较于原始CycleGAN、DCGAN、DCGAN+和WGAN算法，生成病害图像的FID分数分别降低了43.33、32.67、24.24和19.72，GAN-train与GAN-test相较于原始CycleGAN提升了3.13、4.25百分点；采用改进的CycleGAN图像扩充方法构建玉米病害数据集，基于该数据集的玉米叶片病害识别模型准确率在3种网络架构上均得到有效提升：AlexNet提升3.90百分点，VGGNet提升4.41百分点，ResNet提升3.44百分点，在ResNet网络架构上与传统数据增强算法相比病害识别率提升5.79百分点。结果表明，改进的CycleGAN网络有效解决了玉米病害图像数据集匮乏的问题。

Abstract

An improved cycle-consistent adversarial networks （CycleGAN）-based method for augmenting data of maize leaf disease images was designed to solve the difficulties in obtaining image dataset of recognizing maize diseases，insufficient samples，and imbalanced samples across different categories of diseases.Convolutional kernels with smaller receptive fields were used to optimize the structure of the CycleGAN network and generate sample images with high-quality and reduce the occurrence of overfitting.The SE （squeeze-excitation） attention mechanism was embedded into the residual module of the generator to enhance the ability of CycleGAN to extract disease features and allow the network to more accurately capture diseases with small target or features with subtle inter-domain differences.The results showed that the improved CycleGAN reduced the frechet inception distance （FID） value of generated disease images by 43.33，32.67，24.24，and 19.72 compared with the original CycleGAN，DCGAN，DCGAN+，and WGAN algorithms. GAN-train and GAN-test increased by 3.13 and 4.25 percentage points compared to that in the original CycleGAN.The improved CycleGAN-based method for augmenting data was used to construct a dataset of maize diseases，and the accuracy of recognizing the maize leaf diseases based on this dataset was significantly improved on three network architectures.AlexNet，VGGNet，and ResNet increased by 3.90，4.41，and 3.44 percentage points，respectively.Compared with traditional data augmentation algorithms，the disease recognition rate of ResNet network architecture increased by 5.79 percentage points.It is indicated that the improved CycleGAN network effectively solves the problem of insufficient image dataset of maize diseases.

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references

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