ArticleOpen 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.
Keyword
Computer vision, Deep learning, Yam, Defect detection, Automatic grading
References
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Copyright
© 2023 by the author(s).
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND) license, which permits non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited and is not modified or adapted.
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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 Agriculture, 7(2), 182-187.
DOI: http://dx.doi.org/10.26855/ijfsa.2023.06.002