Deep Learning Based Detection and Classification of Tobacco Leaf Diseases Using Convolutional neural networks

Deep learning, a powerful subset of artificial intelligence, has emerged as a transformative tool in various domains, including agriculture, due to its ability to autonomously analyze complex data. This study focuses on the application of Convolutional Neural Networks (CNNs) for detecting and classifying diseases affecting tobacco leaves, a major concern in countries like India, one of the largest producers of tobacco. India produces an estimated 804 million kg of tobacco annually, with cultivation spanning half of its states. Traditional methods relying on manual inspection by farmers are time-consuming, subjective, and often inaccurate, leading to crop losses and reduced productivity. To overcome these challenges, this research developed a deep learning-based model leveraging real-time images of diseased tobacco leaves provided by farmers from the Prakasam district of Andhra Pradesh. A dataset comprising over 1000 annotated images depicting three distinct tobacco leaf diseases was curated for training and validation. The CNN model achieved an impressive accuracy of 95%, demonstrating its ability to accurately detect and classify diseases, enabling targeted interventions. The study not only underscores the efficiency of CNNs in revolutionizing disease detection but also highlights their role in reducing crop losses, improving yield, and supporting farmers with timely and unbiased disease diagnosis. By incorporating advanced image processing techniques and deep learning, this research provides a scalable, automated, and cost-effective solution to a pressing agricultural challenge, paving the way for improved crop management and sustainable farming practices.