A Real Time Tomato Crop Disease Prediction using Deep Learning

This research addresses the persistent need for sophisticated and effective tomato plant disease detection methods within India's agricultural landscape. Tomatoes, vital for culinary richness and economic sustenance, confront significant challenges from bacterial, fungal, and viral infections. Existing detection methods, employing image processing and machine learning, encounter limitations in accuracy, generalization, and real-time applicability. To address these challenges, this research study proposes a novel Tomato Plant Disease Predictor comprising a feature extractor and classifier, integrating real-time imaging and a fine-tuned Convolutional Neural Network (CNN). The methodology aims to overcome current shortcomings and enhance precision, applicability, and generalization. The findings hold the potential for benefiting farmers by revolutionizing agricultural practices, reducing workloads, and promoting sustainable solutions. This study presents a robust approach leveraging cutting-edge technologies, marking a significant step towards proactive and precise crop disease management.