IJIRCSTJournal International Journal of Innovative Research in Computer Science and Technology I S Volume 14 Issue 2 Computer Science English March - April 2026 N N Y 2026 04 09 Computer Sciences Digital Platform for Crop Health and Agricultural Services English Y 60 66 Nanditha H Prasad English Y Sana M English N Sredha Selvam Pereira English N Stephina Stanly English N Aiswarya I P English N https://doi.org/10.55524/ijircst.2026.14.2.8 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. English Deep Learning; Plant Disease Detection; Convolutional Neural Networks; Plantvillage Dataset; Image Classification; Precision Agriculture. https://ijircst.org/abstract.php?article_id=1455