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Locality Preserved Selective Projection Learning for Rice Variety Identification Based on Leaf Hyperspectral Characteristics

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成果类型:
期刊论文
作者:
Long, Chen-Feng;Wen, Zhi-Dong;Deng, Yang-Jun;Hu, Tian;Liu, Jin-Ling;...
通讯作者:
Deng, YJ
作者机构:
[Deng, Yang-Jun; Wen, Zhi-Dong; Long, Chen-Feng; Zhu, Xing-Hui] Hunan Agr Univ, Coll Informat & Intelligence, Changsha 410128, Peoples R China.
[Deng, Yang-Jun; Wen, Zhi-Dong; Long, Chen-Feng; Zhu, Xing-Hui] Hunan Agr Univ, Hunan Prov Engn & Technol Res Ctr Rural & Agr Info, Changsha 410128, Peoples R China.
[Hu, Tian] Hunan Acad Agr Sci, Hunan Agr Equipment Res Inst, Changsha 410125, Peoples R China.
[Liu, Jin-Ling] Hunan Agr Univ, Coll Agron, Changsha 410128, Peoples R China.
通讯机构:
[Deng, YJ ] H
Hunan Agr Univ, Coll Informat & Intelligence, Changsha 410128, Peoples R China.
Hunan Agr Univ, Hunan Prov Engn & Technol Res Ctr Rural & Agr Info, Changsha 410128, Peoples R China.
语种:
英文
关键词:
leaf hyperspectral characteristics;rice variety identification;selective projection learning;support vector machines
期刊:
Agronomy
ISSN:
2073-4395
年:
2023
卷:
13
期:
9
页码:
2401-
基金类别:
Conceptualization, C.-F.L.; Methodology, Y.-J.D.; Validation, Y.-J.D. and J.-L.L.; Formal analysis, X.-H.Z.; Investigation, C.-F.L.; Data curation, Z.-D.W. and T.H.; Writing-original draft, C.-F.L. and Z.-D.W.; Writing-review & editing, Y.-J.D. and J.-L.L.; Visualization, T.H.; Funding acquisition, X.-H.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the Hunan Provincial Key Research and Development Program under Grant 2023NK2011 and the Meizhou Tobacco Science Research Project under Grant No. 202204.
机构署名:
本校为第一且通讯机构
院系归属:
农学院
摘要:
Rice has an important position in China as well as in the world. With the wide application of rice hybridization technology, the problem of mixing between individual varieties has become more and more prominent, so the variety identification of rice is important for the agricultural production, the phenotype collection, and the scientific breeding. Traditional identification methods are highly subjective and time-consuming. To address this issue, we propose a novel locality preserved selective projection learning (LPSPL) method for non-destructive rice variety identification based on leaf hype...

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