{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T21:27:06Z","timestamp":1777066026323,"version":"3.51.4"},"reference-count":22,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,4,18]],"date-time":"2017-04-18T00:00:00Z","timestamp":1492473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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 \u201cgood\u201d and \u201cnot good\u201d (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.<\/jats:p>","DOI":"10.3390\/s17040886","type":"journal-article","created":{"date-parts":[[2017,4,18]],"date-time":"2017-04-18T11:22:04Z","timestamp":1492514524000},"page":"886","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A Novel Auto-Sorting System for Chinese Cabbage Seeds"],"prefix":"10.3390","volume":"17","author":[{"given":"Kuo-Yi","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Bio-Industrial Mechatronics Engineering, National Chung Hsing University, Tai-Chung 402, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian-Feng","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Bio-Industrial Mechatronics Engineering, National Chung Hsing University, Tai-Chung 402, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,4,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.compag.2012.01.004","article-title":"Ripeness estimation of grape berries and seeds by image analysis","volume":"82","author":"Melgosa","year":"2012","journal-title":"Comput. 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