Automated Segregation of Degraded Vegetables in Industrial Settings using YOLO and OpenCV

This research paper focuses on the development of an automated system for segregating degraded vegetables in industrial environments using YOLO (You Only Look Once) and OpenCV. The segregation of degraded vegetables is crucial in industrial environments, particularly in scenarios involving storage and preservation. Storing damaged produce not only increases costs but also affects the quality and shelf life of the entire batch. Prompt identification and removal of degraded vegetables are vital to ensuring consistent quality control, minimizing waste, and maintaining product standards. The proposed solution enhances the effectiveness and precision of vegetable sorting, reducing human errors and labour expenses. YOLO, a real-time object detection algorithm, identifies and classifies vegetables based on their visual quality, while OpenCV, a robust computer vision library, aids in image processing and feature extraction. The system processes images of vegetables on a conveyor belt, leveraging the combined techniques of YOLO and OpenCV to distinguish between fresh and degraded items. This project is designed to be scalable and adaptable. The proposed work is suitable for various types of vegetables and industrial environments. This cost-effective solution improves productivity, reduces operational downtime, and streamlines the quality control process, making it ideal for large-scale vegetable sorting in industrial settings.