江汉大学学报（自然科学版）2025，Vol.53Issue(3)：77-85,9.DOI:10.16389/j.cnki.cn42-1737/n.2025.03.009
基于SimAM-ResNet18的苹果病害叶片分类研究
Apple Disease Leaf Classification Based on SimAM-ResNet18
吴文俊 1陶俊 1隗一凡 1侯顺智 1袁冬华1
作者信息
- 1. 江汉大学 人工智能学院,湖北 武汉 430056
- 折叠
摘要
Abstract
Apple disease leaf classification and recognition are significant for disease monitoring and control improvement in apple plantations.Aiming at the problem of apple disease leaf classification and recognition,this paper proposed a ResNet model based on the SimAM attention mechanism.The model combined the SimAM attention module,Swish activation function,and entropy weight Focal Loss function through migration learning and data enhancement operations to improve the accurate recognition of apple disease leaves with uneven sample distribution.Experimental results showed that the improved SimAM-ResNet18 model achieved an accuracy of 94.68%on the test set,which was a 2.89%improvement compared to the benchmark network ResNet18.Compared to other classical convolutional classification models,AlexNet,VGG16,and GoogLeNet,the model accuracy improved by 7.02%,5.25%,and 4.31%.The results show that the ResNet model based on the SimAM attention mechanism has a high potential for apple disease leaf classification and recognition with uneven sample distribution.关键词
苹果病害叶片/图像分类/迁移学习/SimAM注意力机制/ResNet18Key words
apple disease leaf/image classification/transfer learning/SimAM attention mechanism/ResNet18分类
信息技术与安全科学引用本文复制引用
吴文俊,陶俊,隗一凡,侯顺智,袁冬华..基于SimAM-ResNet18的苹果病害叶片分类研究[J].江汉大学学报（自然科学版）,2025,53(3):77-85,9.
