Abstract

This research proposes a robust deep learning framework for the accurate classification of tomato leaf diseases by leveraging both original and augmented image datasets. The study utilizes a curated set of 1,200 original images spanning six distinct classes, five representing common tomato diseases (Early Blight, Bacterial Spot, Leaf Mold, Yellow Leaf Curl Virus, and Spider Mites) and one healthy class. Through systematic data augmentation, the dataset was expanded to 5,980 samples, enhancing model generalization. A comparative analysis of several convolutional neural network architectures was conducted, including a baseline CNN, a lightweight custom CNN, MobileNetV2, DenseNet121, InceptionV3, and the proposed VGG16-based transfer learning model. The VGG16 model, optimized via fine-tuning, label smoothing, and regularization, achieved the highest accuracy of 99.83%. It demonstrated superior robustness in distinguishing between visually similar disease symptoms. This work reinforces the importance of tailored model architecture and data strategy in agricultural image analysis and contributes to the advancement of intelligent, field-deployable crop health monitoring systems.

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