Research on kiwifruit grading detection system based on depth limit learning machine
Author: 
Liang WangAuthors Info & Claims
Liang WangAuthors Info & ClaimsArticle No.: 98, Pages 1 - 6
Abstract
Aiming at the low recognition rate and low accuracy of traditional kiwifruit grading detection, a feature extraction method based on machine vision kiwifruit detection system is proposed. In order to improve the recognition accuracy, the R component map is used as the input map, and the median filtering method is used to denoise and extract the surface defect features of kiwifruit. On this basis, the least square method is used to establish the ellipse fitting curve to extract the size features, and the mean and standard deviation of H, I, S are used to extract the color features; At the same time, 30 models were selected to establish the deep extreme learning machine (DELM) model to identify kiwifruit defects. Finally, 326 kiwi images were constructed, and the results showed that the correct recognition rate of the depth limit learning machine model was 97.5%, which could achieve accurate grading.
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Published In
December 2022
770 pages
ISBN:9781450398336
DOI:10.1145/3579654
Copyright © 2022 ACM.
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Association for Computing Machinery
New York, NY, United States
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Published: 14 March 2023
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ACAI 2022
ACAI 2022: 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence
December 23 - 25, 2022
Sanya, China
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Overall Acceptance Rate 173 of 395 submissions, 44%
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