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Evaluating different approaches to non-destructive nitrogen status diagnosis of rice using portable RapidSCAN active canopy sensor.
Lu, Junjun; Miao, Yuxin; Shi, Wei; Li, Jingxin; Yuan, Fei.
Afiliação
  • Lu J; International Center for Agro-Informatics and Sustainable Development, College of Resources and Environmental Sciences, China Agricultural University, Beijing, 100193, China.
  • Miao Y; International Center for Agro-Informatics and Sustainable Development, College of Resources and Environmental Sciences, China Agricultural University, Beijing, 100193, China. ymiao2007@gmail.com.
  • Shi W; International Center for Agro-Informatics and Sustainable Development, College of Resources and Environmental Sciences, China Agricultural University, Beijing, 100193, China.
  • Li J; International Center for Agro-Informatics and Sustainable Development, College of Resources and Environmental Sciences, China Agricultural University, Beijing, 100193, China.
  • Yuan F; Department of Geography, Minnesota State University, Mankato, MN, 56001, USA.
Sci Rep ; 7(1): 14073, 2017 10 26.
Article em En | MEDLINE | ID: mdl-29074943
ABSTRACT
RapidSCAN is a new portable active crop canopy sensor with three wavebands in red, red-edge, and near infrared spectral regions. The objective of this study was to determine the potential and practical approaches of using this sensor for non-destructive diagnosis of rice nitrogen (N) status. Sixteen plot experiments and ten on-farm experiments were conducted from 2014 to 2016 in Jiansanjiang Experiment Station of the China Agricultural University and Qixing Farm in Northeast China. Two mechanistic and three semi-empirical approaches using the sensor's default vegetation indices, normalized difference vegetation index and normalized difference red edge, were evaluated in comparison with the top performing vegetation indices selected from 51 tested indices. The results indicated that the most practical and stable method of using the RapidSCAN sensor for rice N status diagnosis is to calculate N sufficiency index with the default vegetation indices and then to estimate N nutrition index non-destructively (R2 = 0.50-0.59). This semi-empirical approach achieved a diagnosis accuracy rate of 59-76%. The findings of this study will facilitate the application of the RapidSCAN active sensor for rice N status diagnosis across growth stages, cultivars and site-years, and thus contributing to precision N management for sustainable intensification of agriculture.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Oryza / Espectrofotometria Infravermelho / Produtos Agrícolas / Nitrogênio Tipo de estudo: Clinical_trials / Diagnostic_studies Idioma: En Revista: Sci Rep Ano de publicação: 2017 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Oryza / Espectrofotometria Infravermelho / Produtos Agrícolas / Nitrogênio Tipo de estudo: Clinical_trials / Diagnostic_studies Idioma: En Revista: Sci Rep Ano de publicação: 2017 Tipo de documento: Article País de afiliação: China
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