International Journal of Innovative Research in Computer Science and Technology
Year: 2026, Volume: 14, Issue: 2
First page : ( 60) Last page : ( 66)
Online ISSN : 2347-5552
DOI: 10.55524/ijircst.2026.14.2.8 |
DOI URL: https://doi.org/10.55524/ijircst.2026.14.2.8
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)http://creativecommons.org/licenses/by/4.0
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Nanditha H Prasad , Sana M, Sredha Selvam Pereira, Stephina Stanly, Aiswarya I P
Modern precision agriculture requires the incorporation of high-accuracy diagnostic instruments to guarantee food security for inexperienced practitioners. This paper introduces an AI-driven agricultural web architecture that connects deep learning-based diagnostics with real-world farm management. The main contribution is a Convolutional Neural Network (CNN) framework that can automatically find diseases in five common crops: Capsicum annuum, Vitis vinifera, Zea mays, Solanum tuberosum, and Solanum lycopersicum. The proposed model reached a final training accuracy of 98.30% and a validation accuracy of 90.12% over 10 epochs by using a sequential architecture with optimized convolutional layers and data augmentation. The platform has a localized marketplace, a government scheme eligibility engine, and a Crop Journal for long-term record-keeping to make it useful in the real world. Results demonstrate that this unified ecosystem provides a transparent and accessible framework for data-informed agricultural management, effectively lowering the technical barrier for new farmers.
B.Tech Scholar, Department of Computer Science & Engineering, Marian Engineering College, Thiruvananthapuram, India
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