Guava leaf disease detection: a powerful approach with vision transformers

Abstract

Guava is one of the most economically important fruits in tropical and subtropical regions like Bangladesh, India, Pakistan and many more. However, its production is significantly impacted by various leaf diseases. In recent years, computer vision techniques have emerged as effective tools for automating disease detection in plants. In this study, we propose a novel approach for guava leaf disease classification using Vision Transformer (ViT), a state-of-the-art deep learning architecture known for its ability to capture long-range dependencies in images. Our dataset comprises images of guava leaves categorized into five classes: Healthy, Canker, Dot, Rust, and Mummification, totaling approximately 4749 images. We first preprocessed the images and then trained a Vision Transformer model on the dataset. The experimental results demonstrate the effectiveness of our approach, with a training accuracy of 97.42%, validation accuracy of 95.60%, and testing accuracy of 97.54%. Furthermore, we conducted comparative experiments with traditional transfer learning models, including ResNet50 model (Training accuracy – 98.76%, Testing accuracy – 95.00%, validation accuracy – 96.10%), VGG19 model (Training accuracy - 90.39%, Testing accuracy - 89%, validation accuracy - 88.77%), EfficientNetB5 model (Training accuracy - 96.49%, Testing accuracy - 94.97%, validation accuracy - 93.33%). While these models have shown promising performance in various computer vision tasks, our results indicate that the Vision Transformer outperforms them in terms of both accuracy and computational efficiency for guava leaf disease classification. The success of our proposed approach underscores the potential of Vision Transformers in agricultural applications, particularly in the early detection and management of plant diseases. Our findings contribute to the ongoing efforts to leverage artificial intelligence for sustainable agriculture, offering a powerful tool for farmers and researchers to combat guava leaf diseases and ensure global food security.