Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Real-Time Intelligent Vegetable Grading Using YOLOv12

Real-Time Intelligent Vegetable Grading Using YOLOv12

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

Paperback

Botany & Horticulture

ISBN10: 6209771254
ISBN13: 9786209771255
Publisher: LAP Lambert Academic Publishing
Published: Mar 26 2026
Pages: 72
Weight: 0.24
Height: 0.17 Width: 6.00 Depth: 9.00
Language: English
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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