Advanced CNN-Based Tomato Leaf Disease Classification: A Deep Learning Approach for Precision Agriculture

Abstract: The early and accurate detection of plant diseases is crucial for maintaining agricultural productivity and food security. This paper presents an advanced Convolutional Neural Network (CNN) architecture for classifying ten distinct tomato leaf diseases with high precision. Utilizing a dataset of 16,021 annotated tomato leaf images from the PlantVillage repository, we developed a six-layer deep CNN model that achieves superior classification performance compared to existing approaches. Our methodology incorporates extensive data augmentation, care- ful hyperparameter tuning, and a systematic evaluation across multiple training epochs (10, 20, and 50). The proposed model demonstrates progressive improvement in classification accuracy, reaching 97% at 50 epochs, with particular strengths in distin- guishing visually similar diseases like early blight and late blight. We further implement a practical web-based interface using Streamlit to facilitate real-world deployment. Comprehensive ex- periments validate our architecture’s effectiveness, with detailed analysis of feature importance and model interpretability. This work contributes to the growing field of precision agriculture by providing farmers with an accessible, automated tool for plant disease diagnosis, potentially reducing crop losses by enabling timely intervention. Index Terms: Convolutional Neural Networks, Deep Learn- ing, Tomato Leaf Disease Classification, Precision Agriculture, Computer Vision, Plant Pathology, Automated Disease Detection