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@article{206805,
        author = {Reeshal Sandra Dmello and Praptha Gatty and Gayana H and Sanidhya Rai and Archana K M and Ashwini Vilas Arjun and Kiran P Acharya},
        title = {Tomato Care AI: A Lightweight Client-Side Deep Learning Framework for Tomato Leaf Disease Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {518-521},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206805},
        abstract = {Early and accurate detection of plant diseases is very important as it helps to boost a farmer’s output and makes the food supply stronger. However, traditional methods of disease identification depend on manual inspection, which is usually time-consuming, subjective, and unavailable for small-scale farmers. Tomato Care AI, a lightweight browser system that employs deep learning to detect tomato leaf disease, is suggested by the study. The system uses image classification methods to detect diseases that often occur on tomato leaves such as Early Blight and Septoria leaf spot. The system learns on labeled leaf image datasets which is classified into three classes (healthy, early blight and septoria spots) and optimized for efficient performance in low resource devices. From the experiments, it is observed that neural network can be very accurate in classifying disease in plants and CNNs can achieve more than 95% accuracy in a controlled environment. In addition to the classification, the system typically offers an examination of diseases, their causes, and preventative actions. The solution designed shows the use of AI in precision farming and how AI systems could be further developed for better real time decision making.},
        keywords = {CNN, Deep Learning, Image Classification, Plant Disease Detection, Precision Agriculture, TensorFlow.js.},
        month = {July},
        }