Smart Farming Model with Vision Transformar: Eggplant Diease Detaction System

Abstract

Eggplant is one of the most commercially important crops in tropical and subtropical regions such as Bangladesh, India, and Pakistan. However, several leaf diseases significantly reduce yield and quality. Traditional disease detection methods are manual, time-consuming, and dependent on expert knowledge, which delays timely intervention. Although machine learning and deep learning approaches like CNN-SVM hybrids, VGG networks, and ResNet-based techniques have been explored, they often face challenges such as poor real-world accuracy, limited generalizability, and high computational costs. This study proposes a Vision Transformer (ViT)-based approach for automated eggplant leaf disease identification to address these issues. ViT, a state-of-the-art architecture, captures both local and global image patterns, making it effective for identifying subtle disease symptoms. We used a dataset of about 9,800 eggplant leaf images classified into seven categories: Wilt, Mosaic Virus, Small Leaf, Insect Pest, Leaf Spot, White Mold, and Healthy. The ViT model, trained with preprocessing and augmentation techniques, achieved 98.16% testing accuracy. For comparison, transfer learning models ResNet50 (91.53%), EfficientNetB5 (91.33%), VGG19 (86.63%), and VGG16 (87.55%) were evaluated, but ViT outperformed them in accuracy and efficiency. The findings suggest that ViT offers a more reliable and scalable solution for precision agriculture, enabling early detection, reduced crop loss, sustainable production, and improved food security.