Vol. 6 No. 2 (2025)

Standard Journal Issues

AppleVit: A Smart Agricultural Software for Apple Leaf Disease Detection Using AI

Published 2025-09-06

  • Pradeep Gupta
    • ,
  • Rakesh Singh Jadon

Pradeep Gupta
Department of CSE, Madhav Institute of Technology & Science, Gwalior (M.P.), India

Rakesh Singh Jadon
Department of CSE, Madhav Institute of Technology & Science, Gwalior (M.P.), India

Abstract

Apple leaf diseases endanger global apple production at such an intensity that it demands precise detection systems to control disease spread effectively. Traditional inspection methods and Convolutional Neural Network (CNN)-based models face challenges when processing extended image dependencies in leaf images, which subsequently affects their ability to identify diseases accurately. This research develops AppleViT, a lightweight Vision Transformer (ViT)-based model that applies Vision Transformer technology with self-attention approaches to enhance leaf disease classification accuracy and feature extraction within apple leaf detection systems. AppleViT was trained using a public dataset comprising 9,714 apple leaf images, categorized into four classes: Apple Scab, Black Rot, Cedar Apple Rust, and Healthy. The accuracy rate of AppleViT reached 97.8%, which exceeded the ResNet-50 and EfficientNet-B3 and MobileNetV3 models while operating with 1.3 million parameters suitable for precision agriculture real-time usage. The proposed approach demonstrates both high generalization skills alongside precise precision and recall value measurements for disease categories. Future research will create attention visualization features and mobile application compatibility before expanding the architecture to identify multiple diseases across different plant types. AppleViT highlights the potential of Vision Transformer (ViT) technology as a powerful tool to revolutionize plant disease detection for improving crop yield and disease management worldwide.

Keywords

Apple Leaf Disease Classification , Deep Learning , Vision Transformer

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