Classification of Okra Leaf Diseases Using Machine Learning

Abstract

In precision agriculture, machine learning models are increasingly revolutionizing disease detection and management for crops. Recent advances in computer vision, particularly Vision Transformer (VIT) architectures, have shown promising results in accurately identifying plant diseases. This study explores three VIT models. variants: Pre-trained VIT, Mobile VIT, and Scratch VIT applied to Okra leaf disease detection, an area where effective early identification can significantly improve crop yield. The dataset, containing 3,775 Okra leaf images includes four disease classes and represents diverse environmental conditions to ensure robust model performance. Among the models, the Pre-trained VIT demonstrated the highest performance, achieving 96% validation and test accuracy with an AUC score of 0.985, indicating strong generalizability and minimal overfitting. Scratch VIT followed closely with 93% validation accuracy, 94% test accuracy, and a 0.98 AUC score, showcasing reliable classification despite being trained from scratch. Mobile VIT achieved 83% validation and 85% test accuracy with an AUC of 0.962, suggesting some limitations in handling complex features. The study highlights the Pre-trained VIT’s potential as a reliable and efficient solution for okra disease detection, providing an effective tool for farmers to make informed, proactive decisions in crop management. This approach emphasizes the value of VIT models in advancing precision agriculture through accurate, disease classification.