Copyright © 2026 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{198593,
        author = {Dr. M Raghava Naidu and G. Sagar babu and K.V.Karthik and A. Bala subramanyam and K.Ganesh Babu},
        title = {Samrudhi - Precision Agriculture Platform},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {10746-10751},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198593},
        abstract = {The given paper shows Samrudhi, a web-based precision agriculture software which unites the real-time weather monitoring and CNN-like crop disease detection under the same roof. Numerous farmers currently have access to weather information or disease notification separately and that disconnect leads to delays in making decisions. Samrudhi resolves this by linking both ends where in times of danger when weather information is provided, disease checks are also provided on the platform in real time. The CNN model was trained using Plant Village images and achieved the accuracy of 98.7 percent when used on test data and produced results within about 2 seconds. It is based on React.js, Django REST Framework and Tailwind CSS and was developed with a focus on smallholder farmers in mind, who should have clear, fast answers, not overwhelming data monitoring dashboard.},
        keywords = {Samrudhi; Precision Agriculture; Convolutional Neural Networks; Agro-Meteorology; Django REST Framework; React.js; Plant Disease Detection; Predictive Analytics.},
        month = {April},
        }