Journal of Science Engineering Technology and Management Science

Timely and accurate crop identification and health monitoring across large agricultural landscapes remain challenging, especially where field-level inspection is impractical due to scale, cost, or terrain. This paper presents an AI/MLbased 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 Sentinel-2 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 land-cover and agricultural classes, complemented by a health-assessment module estimating vegetative condition. The system is delivered through a Flask-based web application with an SQLite backend, offering user authentication, image upload, real-time inference, confidence reporting, historical records, and an administrative dashboard. Experimental evaluation demonstrates classification accuracy between 95-98%, indicating strong generalization across visually similar categories, positioning the framework as a scalable alternative to manual crop surveys for precision agriculture applications.

Keywords : Crop Classification, Convolutional Neural Network, Deep Learning, Remote Sensing, EuroSAT, Satellite Imagery, Precision Agriculture, Crop Health Monitoring, Image Classification, Computer Vision, TensorFlow, Keras, Flask.

Author : Dr. PS. Naveen Kumar, 2Veera Anish Kumar, 3Bhargav, 4Prem Kumar, 5Sai Charan

Title : AI/ML-Based Crop Classification and Health Monitoring Using Remote Sensing Data

Volume/Issue : 2026;03(08)

Page No : 813-821

The rapid growth of satellite and aerial imaging technology has made high-resolution remote sensing data widely available for environmental monitoring, urban planning, defense surveillance, and intelligent transportation. Manual interpretation of such imagery is slow, labourintensive, and prone to human error, motivating the need for automated, learningbased analysis. This paper presents the design and development of an AI/ML-based object detection system for high-resolution remote sensing and aerial imagery, built around the YOLOv8 deep learning architecture. The system is delivered as a secure, web-based platform (Flask, Flask-SQLAlchemy, SQLite) offering two intelligent modules: an Object Detection module that identifies buildings, roads, vehicles and other ground objects from an uploaded satellite/aerial image, and a Traffic Management module that analyses traffic-surveillance video to detect vehicles, estimate traffic density and recommend an appropriate signal state. Each detected object is annotated with a bounding box, class label and confidence score. Experimental results obtained from the deployed prototype show that the system reliably detects multiple object classes—including cars, motorcycles, buses, trucks and pedestrians—under varying scene complexity, and produces real-time, humaninterpretable outputs suitable for practical remote sensing and traffic-monitoring applications.

Keywords : Remote Sensing, Object Detection, YOLOv8, Deep Learning, Satellite Imagery, Aerial Imagery, Traffic Density Estimation, Flask, Computer Vision.

Author : ¹V. Prasad, ²G. Namal Sai, ³D. Anil, ⁴P. Madhan, ⁵U. Kiran Sai

Title : Development of AI/ML Model for Object Detection from High-Resolution Remote Sensing Images

Volume/Issue : 2026;03(08)

Page No : 822-829

Vehicle damage assessment is an essential process in automobile insurance and repair services. Traditional inspection methods are manual, timeconsuming, and prone to errors. This paper presents a Deep Learning-Based Vehicle Damage Assessment System that uses the YOLO (You Only Look Once) object detection algorithm to automatically detect and classify vehicle damages such as dents, scratches, broken glass, and bumper damage from uploaded images. Developed using Python, Flask, OpenCV, TensorFlow/Keras, and SQLite, the system provides fast, accurate, and reliable damage detection by highlighting damaged regions with bounding boxes and labels. It reduces inspection time, minimizes human intervention, and supports efficient insurance claim processing and repair estimation, making it a practical solution for intelligent vehicle damage assessment.

Keywords : Deep Learning, Vehicle Damage Assessment, YOLO, Computer Vision, Object Detection, Image Processing, Flask, TensorFlow, OpenCV, Artificial Intelligence.

Author : 1D. Madhuri,2Damarla Keerthi,3Bapatla Chandini,4Dasari Rajeswari

Title : DEEP LEARNING – BASED VEHICLE DAMAGE ASSESSMENT SYSTEM

Volume/Issue : 2026;03(08)

Page No : 830-836