Dr. PS. Naveen Kumar, 2Veera Anish Kumar, 3Bhargav, 4Prem Kumar, 5Sai Charan 03 08 Timely and accurate crop identification and health monitoring across large agricultural landscapes remain challenging especially where fieldlevel inspection is impractical due to scale cost or terrain This paper presents an AIMLbased system for automated crop classification and health monitoring using satellite remote sensing imagery A Convolutional Neural Network CNN trained on the EuroSAT benchmark dataset learns discriminative spatial and spectral features directly from Sentinel2 derived images eliminating dependency on handcrafted features and manual interpretation The architecture integrates image preprocessing a deep convolutional feature extractor and a softmax classification layer to categorize imagery into landcover and agricultural classes complemented by a healthassessment module estimating vegetative condition The system is delivered through a Flaskbased web application with an SQLite backend offering user authentication image upload realtime inference confidence reporting historical records and an administrative dashboard Experimental evaluation demonstrates classification accuracy between 9598 indicating strong generalization across visually similar categories positioning the framework as a scalable alternative to manual crop surveys for precision agriculture applications Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026