Abstract: Tomato plants are vulnerable to numerous fungal, bacterial, and viral diseases that severely impact crop yield and farm productivity. Early and accurate disease identification is essential to prevent spread and reduce economic losses. Traditional manual inspection methods are time-consuming and impractical at scale, highlighting the need for automated solutions. This research presents a real-time tomato leaf disease classification and treatment recommendation system using deep learning. Four architectures are evaluated comparatively: CNN, MobileNetV2, ResNet50, and EfficientNetB0. EfficientNetB0 achieves the highest classification accuracy and is deployed for live webcam-based inference. A rule-based recommendation module delivers disease-specific guidance covering chemical, organic, and preventive management strategies. The integrated system provides farmers with a reliable, end-to-end decision-support tool for early diagnosis and effective crop management.

Keywords: Tomato Leaf Disease Detection, Deep Learning, Transfer Learning, Convolutional Neural Networks (CNN), MobileNetV2, ResNet50, EfficientNetB0, Real-Time Disease Classification, Treatment Recommendation System, Precision Agriculture.

Downloads: | DOI: 10.17148/IJIREEICE.2026.14418