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A model for phenotyping crop fractional vegetation cover using imagery from unmanned aerial vehicles.
Wan, Liang; Zhu, Jiangpeng; Du, Xiaoyue; Zhang, Jiafei; Han, Xiongzhe; Zhou, Weijun; Li, Xiaopeng; Liu, Jianli; Liang, Fei; He, Yong; Cen, Haiyan.
Afiliação
  • Wan L; College of Biosystems Engineering and Food Science, and State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou, China.
  • Zhu J; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China.
  • Du X; College of Biosystems Engineering and Food Science, and State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou, China.
  • Zhang J; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China.
  • Han X; College of Biosystems Engineering and Food Science, and State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou, China.
  • Zhou W; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China.
  • Li X; College of Biosystems Engineering and Food Science, and State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou, China.
  • Liu J; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China.
  • Liang F; Department of Biosystems Engineering, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon, Kangwon, South Korea.
  • He Y; College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China.
  • Cen H; Institute of Soil Science, Chinese Academy of Sciences, Nanjing, China.
J Exp Bot ; 72(13): 4691-4707, 2021 06 22.
Article em En | MEDLINE | ID: mdl-33963382
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Oryza / Tecnologia de Sensoriamento Remoto Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Oryza / Tecnologia de Sensoriamento Remoto Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article