Prediction of crude protein content in rice grain with canopy spectral reflectance

H. Zhang, T.Q. Song, K.L. Wang, G.X. Wang, H. Hu and F.P. Zeng
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H. Zhang: Key Laboratory of Agro-Ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, P.R. China
T.Q. Song: Key Laboratory of Agro-Ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, P.R. China
K.L. Wang: Key Laboratory of Agro-Ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, P.R. China
G.X. Wang: Institute of Agricultural Ecological Research, College of Life Sciences, Zhejiang University, Hangzhou, P.R. China
H. Hu: Key Laboratory of Digital Agriculture, Institute of Digital Agricultural Research, Zhejiang Academy of Agricultural Sciences, Hangzhou, P.R. China
F.P. Zeng: Key Laboratory of Agro-Ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, P.R. China

Plant, Soil and Environment, 2012, vol. 58, issue 11, 514-520

Abstract: Non-destructive and rapid monitoring methods for crude protein content (CPC) in rice grain are of significance in nitrogen diagnosis and grain quality monitoring, and in enhancing nutritional management and use efficiency. In this study, CPC and canopy spectra in rice were measured based on rice field experiment. Key spectral bands were selected by principal component analysis (PCA) method, and the predicted models were built by multiple linear regressions (MLR), artificial neural network (ANN) and partial least squares regression (PLSR). The results showed that there is a significant correlation between CPC content and key spectral bands. The results of prediction for the three models were in order of PLSR > ANN > MLR with correlation values of 0.96, 0.92 and 0.90, respectively, for the validation data. Therefore, it is implied that CPC in rice (grain quality) could be estimated by canopy spectral data.

Keywords: nitrogen content; principal component analysis; partial least squares regression; artificial neural networks; key spectral bands (search for similar items in EconPapers)
Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:caa:jnlpse:v:58:y:2012:i:11:id:526-2012-pse

DOI: 10.17221/526/2012-PSE

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