Real-Time Intelligent Vegetable Grading Using YOLOv12 | Real-Time Vegetable Freshness and Quality Grading Using YOLO-Based Deep Learning and Computer Vision Techniques

Senthil G A (u. a.)

ISBN 10: 6209771254 / ISBN 13: 9786209771255

Published by LAP LAMBERT Academic Publishing, 2026

About this Item

Real-Time Intelligent Vegetable Grading Using YOLOv12 | Real-Time Vegetable Freshness and Quality Grading Using YOLO-Based Deep Learning and Computer Vision Techniques | Senthil G A (u. a.) | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786209771255 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 135057080

Bibliographic Details

• Title: Real-Time Intelligent Vegetable Grading Using YOLOv12 | Real-Time Vegetable Freshness and Quality Grading Using YOLO-Based Deep Learning and Computer Vision Techniques
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2026
• Language: English
• ISBN 10: 6209771254
• ISBN 13: 9786209771255
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 5D millimeters 220 millimeters width by 150 millimeters height by 5 millimeters depth
• Weight: 125 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.