{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T06:04:24Z","timestamp":1671689064630},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643683683","type":"print"},{"value":"9781643683690","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T00:00:00Z","timestamp":1670889600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,12,13]]},"abstract":"<jats:p>The production technology of Citrus in China is still at the initial stage of development, and the classification of Citrus mainly depends on manual work, which is subjective and inefficient. To solve the problem of time-consuming, laborious and low efficiency of citrus classification, this paper uses the OpenCV image processing technology, Candy edge detection operator for edge detection and DP algorithm for contour extraction to find the two points with the largest distance in the contour to achieve citrus diameter detection; The RGB color space is converted into HSV color space, and the parameters of H component are extracted to obtain the citrus coloring rate, thus realizing the citrus appearance quality grading system. The experiment proves that the accuracy of diameter detection is more than 99%, reducing the artificial classification deviation.<\/jats:p>","DOI":"10.3233\/faia220540","type":"book-chapter","created":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T07:59:59Z","timestamp":1671609599000},"source":"Crossref","is-referenced-by-count":0,"title":["Citrus Appearance Quality Grading System Based on OpenCV Image Processing"],"prefix":"10.3233","author":[{"given":"Yiqin","family":"Bao","sequence":"first","affiliation":[{"name":"College of Information Engineering of Nanjing XiaoZhuang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qin","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information Engineering of Nanjing XiaoZhuang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yulu","family":"Bao","sequence":"additional","affiliation":[{"name":"Nanjing RuiHuaTeng Intellectual Property Co., Ltd., China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Proceedings of CECNet 2022"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA220540","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T08:00:01Z","timestamp":1671609601000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA220540"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,13]]},"ISBN":["9781643683683","9781643683690"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia220540","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,13]]}}}