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International Journal of Food Science and Agriculture

ISSN Print: 2578-3467 Downloads: 207666 Total View: 2912484
Frequency: quarterly ISSN Online: 2578-3475 CODEN: IJFSJ3
Email: ijfsa@hillpublisher.com
Article Open Access http://dx.doi.org/10.26855/ijfsa.2023.06.002

Research on Defect Detection and Automatic Grading of Chinese Yam Using Computer Vision Combined with Deep Learning Methods

Tianzhi Cao

Hokkaido University, Sapporo, Hokkaido, Japan.

*Corresponding author: Tianzhi Cao

Published: July 24,2023

Abstract

With the increasing importance of the quality and safety of agricultural products, the method of combining computer vision and deep learning has been widely used in the detection and automatic grading of agricultural Product defect defects. This study aims to explore the application of computer vision and deep learning in yam defect detection and automatic grading. Firstly, the problems and challenges in yam defect detection and grading were analyzed. Based on this, combined with the requirements of yam defect detection and grading, the specific applications of computer vision and deep learning were further explored, which helps to promote the continuous deepening of the application of computer vision and deep learning in yam defect detection and automatic grading, and thus promotes the continuous improvement of yam sorting efficiency.

References

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How to cite this paper

Research on Defect Detection and Automatic Grading of Chinese Yam Using Computer Vision Combined with Deep Learning Methods

How to cite this paper:  Tianzhi Cao. (2023) Research on Defect Detection and Automatic Grading of Chinese Yam Using Computer Vision Combined with Deep Learning Methods. International Journal of Food Science and Agriculture7(2), 182-187.

DOI: http://dx.doi.org/10.26855/ijfsa.2023.06.002