井冈山大学学报（自然科学版）2025，Vol.46Issue(2)：68-80,13.DOI:10.3969/j.issn.1674-8085.2025.02.009
基于GAN与ResNet34-UNet的黄瓜叶片病斑分割方法
CUCUMBER LEAF SPOT SEGMENTATION METHOD BASED ON GAN AND ResNet34-UNet
摘要
Abstract
In view of the problems of incomplete segmentation and difficult segmentation of small area lesions in the existing segmentation methods for leaf lesions,a leaf spot segmentation method based on improved Generative Adversarial Network(GAN)was introduced,focusing on enhancing the capability of crop disease prevention and control by intelligent spot segmentation.This model consisted of a generator and a discriminator.The generator network,ResNet34-UNet,consisted of U-Net and improved ResNet34,attention mechanisms.It was primarily used to generate more realistic segmentation results to deceive the discriminator,and the discriminator network was composed of a deep convolutional neural network,tasked with distinguishing between the generated segmentation results and the real labels.The experiments showed that the method could achieve good segmentation of leaf lesions,the evaluation metrics as sensitivity,specificity,dice,and accuracy reached 90.36％,97.72％,85.25％,and 97.10％respectively.Compared with the other segmentation networks,the proposed method retained more detailed information,and could achieve more complete segmentation for small areas of disease spots.It could provide strong support for cucumber disease identification and prevention management.关键词
叶片病斑分割/GAN/U-Net/深度学习/注意力机制Key words
leaf lesion segmentation/GAN/U-Net/deep learning/attention mechanism分类
信息技术与安全科学引用本文复制引用
唐卫东,陈冠华,王瑞,刘秋明,刘振文..基于GAN与ResNet34-UNet的黄瓜叶片病斑分割方法[J].井冈山大学学报（自然科学版）,2025,46(2):68-80,13.基金项目
国家自然科学基金项目(31860574) （31860574）
江西省自然科学基金项目(20224BAB205025) （20224BAB205025）
