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@article{206747,
        author = {Mr. Arvindh Naik and Kavya A and Manupriya},
        title = {Tomato Leaf Disease Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {292-295},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206747},
        abstract = {Tomatoes are cultivated extensively across the globe and are essential to food supply chains and agricultural economies. However, they are highly susceptible to foliar diseases such as early blight, bacterial spot, and powdery mildew, which can severely affect productivity and farmer income. Detecting these diseases at an early stage is vital to ensuring healthy crop outcomes. Manual methods for disease identification often require expert intervention, which is time-consuming and difficult to scale in rural regions. This paper proposes an artificial intelligence-based detection system that utilizes deep learning, specifically a pre-trained ResNet50 model, to classify tomato leaf diseases. The model, trained using a labeled dataset, achieves a classification accuracy of 92%. A web interface developed with Flask facilitates real-time user interaction, enabling image uploads and delivering immediate predictions with recommended actions. The system is designed to be user-friendly, accessible, and efficient, making it a practical tool for farmers. Future developments may include broader disease classification, improved model adaptability, and mobile integration.},
        keywords = {CNN, Computer Vision, Deep Learning, Flask, ResNet50, Smart Agriculture, Tomato Disease Detection.},
        month = {July},
        }