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Open AccessArticle

Hyperspectral-Based Estimation of Leaf Nitrogen Content in Corn Using Optimal Selection of Multiple Spectral Variables

1
National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
2
Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China
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Authors to whom correspondence should be addressed.
Sensors 2019, 19(13), 2898; https://doi.org/10.3390/s19132898
Received: 19 May 2019 / Revised: 18 June 2019 / Accepted: 19 June 2019 / Published: 30 June 2019
(This article belongs to the Special Issue Advanced Sensors in Agriculture)
Accurate and dynamic monitoring of crop nitrogen status is the basis of scientific decisions regarding fertilization. In this study, we compared and analyzed three types of spectral variables: Sensitive spectral bands, the position of spectral features, and typical hyperspectral vegetation indices. First, the Savitzky-Golay technique was used to smooth the original spectrum, following which three types of spectral parameters describing crop spectral characteristics were extracted. Next, the successive projections algorithm (SPA) was adopted to screen out the sensitive variable set from each type of parameters. Finally, partial least squares (PLS) regression and random forest (RF) algorithms were used to comprehensively compare and analyze the performance of different types of spectral variables for estimating corn leaf nitrogen content (LNC). The results show that the integrated variable set composed of the optimal ones screened by SPA from three types of variables had the best performance for LNC estimation by the validation data set, with the values of R2, root means square error (RMSE), and normalized root mean square error (NRMSE) of 0.77, 0.31, and 17.1%, and 0.55, 0.43, and 23.9% from PLS and RF, respectively. It indicates that the PLS model with optimally multitype spectral variables can provide better fits and be a more effective tool for evaluating corn LNC. View Full-Text
Keywords: hyperspectral; leaf nitrogen content (LNC); successive projections algorithm (SPA); partial least squares (PLS) model; random forest (RF) model hyperspectral; leaf nitrogen content (LNC); successive projections algorithm (SPA); partial least squares (PLS) model; random forest (RF) model
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Fan, L.; Zhao, J.; Xu, X.; Liang, D.; Yang, G.; Feng, H.; Yang, H.; Wang, Y.; Chen, G.; Wei, P. Hyperspectral-Based Estimation of Leaf Nitrogen Content in Corn Using Optimal Selection of Multiple Spectral Variables. Sensors 2019, 19, 2898.

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