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@article{196663,
        author = {Apurva Anilrao Landge and Janhavi Shivhari Bhombe and Jitika Sandeep Gupta and Chetna Kishor Khetwani and Vaishnavi Wamanrao Titurkar and Prof. Pallavi H. Dhole},
        title = {Tomato Crop Analysis using machine learning and AI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {4996-5003},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196663},
        abstract = {Agriculture forms the backbone of many economies, especially in regions where a significant share of the population relies on farming for income and survival. Within this context, tomatoes have become a staple crop, grown almost everywhere thanks to their versatility and market demand. But tomato crops face a constant threat: disease. Fungal, bacterial, and viral infections can wreck entire harvests, leading to economic losses and threatening food security. Farmers often spot these problems too late, simply because they lack access to expert advice or aren’t trained to recognize early symptoms.This project tackles that gap head-on. It introduces an intelligent detection system that harnesses the power of image processing and deep learning to spot tomato plant diseases early. At the core, there’s a Convolutional Neural Network (CNN) built on EfficientNet that scans leaf images for signs of disease. For the fruits themselves, a YOLO-based model steps in to identify and classify infections. By covering both leaves and fruits, the system casts a wider net— delivering a more thorough diagnosis than single- method tools.
Of course, disease detection alone isn’t enough. Farmers need actionable information. That’s why the system goes further: it generates clear, concise details about each disease, outlines its main symptoms, suggests preventive steps, and points to possible treatments. This knowledge empowers farmers, giving them confidence to respond quickly and minimize damage. Everything works through a user-friendly web interface—no technical background is required. Farmers simply upload an image, and in moments, they receive accurate results and practical guidance. Tests confirm the system’s reliability. The models consistently deliver high accuracy and fast response times, traits essential for real-world deployment. In the field, that translates to immediate, usable help instead of lengthy delays or guesswork. Ultimately, this work aims to bridge the knowledge gap for farmers, leveraging modern artificial intelligence to safeguard crops and, by extension, support global food security. By making disease management simple and accessible, this system brings sophisticated diagnostic tools directly to those who need them most.},
        keywords = {Tomato Disease Detection, Deep Learning, CNN, YOLO, Image Processing, Smart Agriculture.},
        month = {April},
        }