Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{176056,
        author = {Sivadhanush M and Soundaryan M and Naveen S and Senthoor Murugan M},
        title = {AI-DRIVEN SMART IRRIGATION SYSTEM FOR PRECISION AGRICULTURE},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {4780-4784},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176056},
        abstract = {Agriculture remains vital for food security and economic stability, yet farmers face challenges like unpredictable weather, soil degradation, and plant diseases. This project introduces an intelligent system leveraging Machine Learning (ML) to enhance decision-making in crop selection and disease detection. By collecting real-time data on temperature, humidity, soil pH, and moisture, ML algorithms predict optimal crops for specific regions and seasons, promoting sustainable practices and resource optimization. For disease management, the system employs an ESP32CAM module to capture plant images, which are analyzed using a Convolutional Neural Network (CNN) trained on extensive datasets to accurately identify diseases and recommend treatments. This dual approach empowers farmers with timely, data-driven insights, reducing manual efforts and minimizing crop losses. Integrating these technologies fosters sustainable agriculture, enhances productivity, and supports rural livelihoods by providing accessible tools for informed decision-making.},
        keywords = {},
        month = {April},
        }