Author : Mrs.muddana Sarada, SRIRAM V V V N S M PRANEETA, TADI DIMPLE LAKSHMI PRIYA, TALLURI HARSHITHA
Abstract :
Fruit classification and detection play a crucial role in smart agriculture, retail automation, and food quality monitoring systems. This work presents an intelligent fruit recognition system using the YOLO deep learning model integrated with a fullstack web application for real-time deployment. The system captures fruit images through camera or uploaded input and processes them with a trained YOLO model to classify type, detect quality, and estimate ripeness level. The proposed framework supports multiple fruit categories with high accuracy, fast inference speed, and robust detection in challenging lighting backgrounds. The fullstack platform provides seamless interaction using a responsive front-end, REST APIs, and cloud-based backend to manage model execution. The system helps farmers, vendors, and industries automatesorting, reduce manual error, and improve efficiency. Real-time visualization enables instant results with bounding boxes and classification labels. The project demonst
