A Customised Vision Transformer for Accurate Detection and Classification of Java Plum Leaf Disease

Abstract

Vision Transformer (ViT) has recently attracted significant attention for its performance in image classification. However, studies have yet to explore its potential in detecting and classifying plant leaf disease. Most existing research on diseased plant leaf detection has focused on non-transformer convolutional neural networks (CNN). Moreover, the studies that applied ViT narrowly experimented using hyperparameters such as image size, patch size, learning rate, attention head, epoch, and batch size. However, these hyperparameters significantly contribute to the model performance. Recognising the gap, this study applied ViT to Java Plum disease detection using optimised hyperparameters. To harness the performance of ViT, this study presents an experiment on Java Plum leaf disease detection. Java Plum leaf diseases significantly threaten agricultural productivity by negatively impacting yield and quality. Timely detection and diagnosis are essential for successful crop management. The primary dataset collected in Bangladesh includes six classes, ‘Bacterial Spot’, ‘Brown Blight’, ‘Powdery Mildew’, and ‘Sooty Mold’, ‘healthy’, and ‘dry’. This experiment contributes to a thorough understanding of Java Plum leaf diseases. Following rigorous testing and refinement, our model demonstrated a significant accuracy rate of 97.51%. This achievement demonstrates the possibilities of using deep-learning tools in agriculture and inspires further research and application in this field. Our research offers a foundational model to ensure crop quality by precise detection, instilling confidence in the global Java Plum market.