{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T18:25:22Z","timestamp":1783189522504,"version":"3.54.6"},"reference-count":16,"publisher":"World Scientific Pub Co Pte Ltd","issue":"15","funder":[{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["2016YFD0300509"],"award-info":[{"award-number":["2016YFD0300509"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["2018YFD0301005"],"award-info":[{"award-number":["2018YFD0301005"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2022,12,15]]},"abstract":"<jats:p> Rice variety identification is important for genetic breeding classification and crop yield estimation. Traditional identification methods are time-consuming and inaccurate. This paper proposes a method for rice variety identification based on the hyperspectral characteristics of leaves. Hyperspectral data of rice leaves were collected using a geophysical spectrometer imaging system. To reduce the redundance among the hyperspectral data and save the identification cost, locality preserving projections (LPP) is first applied to extract low-dimensional representative features from the leaf hyperspectral data. Then, support vector machine (SVM) is combined for conducting the identification of rice varieties. The experimental results show that the identification rate of 10 varieties of early rice was found to be 91.67% and the identification rate of 10 varieties of late rice was 97.33%. <\/jats:p>","DOI":"10.1142\/s0218001423500015","type":"journal-article","created":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T14:18:07Z","timestamp":1670422687000},"source":"Crossref","is-referenced-by-count":10,"title":["Rice Variety Identification Based on the Leaf Hyperspectral Feature via LPP-SVM"],"prefix":"10.1142","volume":"36","author":[{"given":"Tian","family":"Hu","sequence":"first","affiliation":[{"name":"College of Agronomy, Hunan Agricultural University, Changsha, P.\u00a0R.\u00a0China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5646-6553","authenticated-orcid":false,"given":"Yineng","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Information Science & Engineering, Hunan Women\u2019s University, Changsha, P.\u00a0R.\u00a0China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Di","family":"Li","sequence":"additional","affiliation":[{"name":"Information & Network Center, Hunan Agricultural University, Changsha, P.\u00a0R.\u00a0China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenfeng","family":"Long","sequence":"additional","affiliation":[{"name":"College of Information & Intelligence, Hunan Agricultural University, Changsha, P.\u00a0R.\u00a0China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhidong","family":"Wen","sequence":"additional","affiliation":[{"name":"College of Information & Intelligence, Hunan Agricultural University, Changsha, P.\u00a0R.\u00a0China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rong","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Information & Intelligence, Hunan Agricultural University, Changsha, P.\u00a0R.\u00a0China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanghui","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Agronomy, Hunan Agricultural University, Changsha, P.\u00a0R.\u00a0China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"219","published-online":{"date-parts":[[2023,1,9]]},"reference":[{"key":"S0218001423500015BIB001","first-page":"551","volume":"7","author":"Bianco G. 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