Real-Time Intelligent Vegetable Grading Using YOLOv12

Bys: G. a., Senthil, J, Gowrisankar, S. K., Ajaykumar

The project focuses on developing a real-time vegetable freshness and quality grading system using advanced deep learning and computer vision techniques. By integrating the YOLOv12 object detection model with Convolutional Neural Networks (CNN), the system can accurately identify vegetables and classify them based on their freshness and quality levels. The approach leverages image processing methods to extract important features such as color, texture, and surface defects, enabling efficient grading without human intervention. This automated system improves speed, consistency, and accuracy compared to traditional manual methods, making it highly suitable for modern smart agriculture and supply chain applications. Ultimately, the proposed solution contributes to reducing food waste, enhancing quality control, and supporting sustainable agricultural practices.

• Format: Paperback
• Language: English
• ISBN: 6209771254
• ISBN13: 9786209771255
• Release Date: March 2026
• Publisher: LAP Lambert Academic Publishing
• Length: 72 Pages
• Weight: 0.24 lbs.
• Dimensions: 0.2" x 6.0" x 9.0"