• Kavya A
• Manupriya
• prof . Sanjaykumar B M

Tomatoes are one of the most widely cultivated crops across the world and play a crucial role in food security and the agricultural economy. However, the occurrence of leaf diseases such as early blight, bacterial spot, and powdery mildew significantly affects plant health, resulting in reduced yield and financial losses for farmers. Early and accurate identification of these diseases is essential for effective crop management. Traditional disease detection methods rely on manual inspection by agricultural experts, which is time-consuming, labour intensive, and often impractical for small-scale or remote farmers. This paper presents an AI-based system that automates the detection of tomato leaf diseases using deep learning techniques. A pre-trained ResNet50 model is utilized through transfer learning to classify tomato leaf images into disease categories. The model is trained on a dataset of labelled images and achieves a classification accuracy of 92%, demonstrating its reliability and effectiveness. The system is deployed via a web-based interface developed using Flask for backend integration and HTML, CSS, and JavaScript for the frontend. Users can easily upload images and receive real-time disease predictions along with actionable treatment recommendations. The system addresses key challenges in agricultural disease management by providing a cost-effective, scalable, and accessible solution that does not require technical expertise from the end-user. Its intuitive interface and lightweight design make it suitable for practical use by farmers in various environments. Future enhancements include expanding the dataset for broader disease coverage, improving model generalization, and developing a mobile-friendly version to extend its reach to more users.

Tomato leaf disease, CNN, ResNet50, machine learning, image classification, Flask, web interface, smart farming.

"TOMATO PLANT DISEASE PREDICTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.312-316, July-2025, Available :http://www.jetir.org/papers/JETIRGX06057.pdf

"TOMATO PLANT DISEASE PREDICTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp312-316, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06057.pdf