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@article{178564,
        author = {Dhanalakshmi R and Harshitha N and Nikita Panigrahi and Poojashree S and Mrs. B Sumangala},
        title = {Agronomic Monitoring Platform- A review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4191-4196},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178564},
        abstract = {The increasing incidence of plant diseases poses a significant threat to global agricultural productivity, especially for small-scale farmers who often lack timely access to expert guidance. To address this challenge, our project presents an enhanced deep learning-based plant disease detection system, The core of our system utilizes a Convolutional Neural Network (CNN) architecture trained on a comprehensive dataset of leaf images to accurately classify and identify plant diseases. This baseline model is augmented with two novel features: (1) real-time disease detection through web camera integration, allowing farmers to instantly analyse affected crops in-field; and (2) an AI-powered chatbot assistant capable of interacting in multiple local languages to provide contextual guidance, disease information, and treatment suggestions. This integrated solution aims to democratize agricultural expertise, reduce crop loss, and promote sustainable farming practices. Preliminary results demonstrate a high classification accuracy on test datasets and positive feedback from usability testing with end-users. Our approach highlights the transformative potential of AI in precision agriculture by combining robust image recognition with accessible farmer-oriented interfaces.},
        keywords = {Deep learning, plant leaf disease detection, visualization, small sample, hyperspectral imaging.},
        month = {May},
        }