A Deep Transfer Learning Framework Using Pre-Trained CNN Architectures to Classify Tomato Leaf Diseases

Abstract

Farmers produce a lot of tomatoes, hence early identification and tomato leaf disease diagnosis can increase production. The information used in this research is public ally available tomato leaf image dataset with multiple disease classifications to create and improve the model. This work employs a methodical preparation workflow that includes picture scaling, normalization, and augmentation to increase resilience to variations in illumination, background clutter, and leaf texture. Two improved deep learning models, Residual and MobileNetV2, the processed pictures to enable efficient mining of significant spatial and texture information related to illness patterns. The experiment's results demonstrate the framework's utility and reliability: MobileNetV2 showed precision of 95.11, accuracy of 95.01, F1 of 95.00, and recall of 95.01 in contrast. The residual model showed 97.76 accuracy, 97.78 precision, 97.76 recall, and 97.64 F1. For every evaluation metric, these perform better than conventional DL architectures like VGG16, CNN, and ResNet. impacting early identification and improved agricultural performance.