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Sensors 2017, 17(4), 886;

A Novel Auto-Sorting System for Chinese Cabbage Seeds

Department of Bio-Industrial Mechatronics Engineering, National Chung Hsing University, Tai-Chung 402, Taiwan
Author to whom correspondence should be addressed.
Academic Editor: Cheng-Chi Wang
Received: 11 February 2017 / Revised: 9 April 2017 / Accepted: 14 April 2017 / Published: 18 April 2017
(This article belongs to the Special Issue Innovative Sensing Control Scheme for Advanced Materials)
Full-Text   |   PDF [7113 KB, uploaded 18 April 2017]   |  


This paper presents a novel machine vision-based auto-sorting system for Chinese cabbage seeds. The system comprises an inlet-outlet mechanism, machine vision hardware and software, and control system for sorting seed quality. The proposed method can estimate the shape, color, and textural features of seeds that are provided as input neurons of neural networks in order to classify seeds as “good” and “not good” (NG). The results show the accuracies of classification to be 91.53% and 88.95% for good and NG seeds, respectively. The experimental results indicate that Chinese cabbage seeds can be sorted efficiently using the developed system. View Full-Text
Keywords: Chinese cabbage seeds; machine vision; auto-sorting Chinese cabbage seeds; machine vision; auto-sorting

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Huang, K.-Y.; Cheng, J.-F. A Novel Auto-Sorting System for Chinese Cabbage Seeds. Sensors 2017, 17, 886.

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