Xiaosong Wang and Jun Shen
Data augmentation, apple leaf diseases, vision transformers, classimbalance∗ College of Management Science and Engineering, ShandongTechnology and Business University, Yantai, Shandong, China;e-mail: [email protected]∗∗ School of Computing and Information Technology, Univer-sity of Wollongong, Wollongong, NSW, Australia; e-mail:[email protected] author: Xiaosong WangRecommended by Teng Li
The health status of apple leaves directly affects the quality and yield of apples, with foliar diseases posing a serious threat to overall production. Therefore, there is an urgent need for a rapid and accurate method for the classification and identification of apple leaf diseases to enable early and automated detection. This study utilises the Plant Pathology 2020 dataset and proposes a novel classification framework that integrates advanced vision transformers (ViTs) with convolutional neural networks (CNNs), enhanced by adaptive data augmentation strategies to improve classification performance. To address the inherent issue of class imbalance within the dataset, the synthetic minority oversampling technique (SMOTE) is employed to effectively augment the minority class samples. The proposed framework adopts a multi-stage training process to enhance feature representation and model robustness, enabling the effective capture of both local lesion patterns and global leaf characteristics. Experimental results demonstrate the stability and high performance of the proposed method, achieving a classification accuracy of 98.96%. Compared with traditional CNN architectures such as ResNet-50, DenseNet-121, GoogLeNet, InceptionV3, and VGG16, the proposed method exhibits significant improvements in both accuracy and generalisation across various augmented datasets. The findings indicate that the framework offers a feasible, reliable, and scalable solution for plant disease detection in the context of precision agriculture.
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