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

G A, Senthil; J, Gowrisankar; S K, Ajaykumar

 
9786209771255: Real-Time Intelligent Vegetable Grading Using YOLOv12: Real-Time Vegetable Freshness and Quality Grading Using YOLO-Based Deep Learning and Computer Vision Techniques

Inhaltsangabe

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.

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Über die Autorin bzw. den Autor

Dr. Senthil G. A is a Professor in the Department of Information Technology at Agni College of Technology, Chennai, with expertise in AI, Machine Learning, Blockchain, IoT, and emerging technologies, actively guiding research and academic innovation. Gowrisankar J and Ajay Kumar S K are undergraduate scholars specializing in AI-based solutions.

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