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Open AccessArticle

Quantitative Analysis of Soil Total Nitrogen Using Hyperspectral Imaging Technology with Extreme Learning Machine

by Hongyang Li 1,2, Shengyao Jia 2 and Zichun Le 3,*
1
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
2
College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China
3
College of Science, Zhejiang University of Technology, Hangzhou 310023, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(20), 4355; https://doi.org/10.3390/s19204355
Received: 29 July 2019 / Revised: 4 October 2019 / Accepted: 7 October 2019 / Published: 9 October 2019
(This article belongs to the Special Issue Advanced Sensors in Agriculture)
Soil nutrient detection is important for precise fertilization. A total of 150 soil samples were picked from Lishui City. In this work, the total nitrogen (TN) content in soil samples was detected in the spectral range of 900–1700 nm using a hyperspectral imaging (HSI) system. Characteristic wavelengths were extracted using uninformative variable elimination (UVE) and the successive projections algorithm (SPA), separately. Partial least squares (PLS) and extreme learning machine (ELM) were used to establish the calibration models with full spectra and characteristic wavelengths, respectively. The results indicated that the prediction effect of the nonlinear ELM model was superior to the linear PLS model. In addition, the models using the characteristic wavelengths could also achieve good results, and the UVE–ELM model performed better, having a correlation coefficient of prediction (rp), root-mean-square error of prediction (RMSEP), and residual prediction deviation (RPD) of 0.9408, 0.0075, and 2.97, respectively. The UVE–ELM model was then used to estimate the TN content in the soil sample and obtain a distribution map. The research results indicate that HSI can be used for the detection and visualization of the distribution of TN content in soil, providing a basis for future large-scale monitoring of soil nutrient distribution and rational fertilization. View Full-Text
Keywords: hyperspectral imaging; soil total nitrogen; partial least squares; extreme learning machine; uninformative variable elimination; successive projections algorithm hyperspectral imaging; soil total nitrogen; partial least squares; extreme learning machine; uninformative variable elimination; successive projections algorithm
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Li, H.; Jia, S.; Le, Z. Quantitative Analysis of Soil Total Nitrogen Using Hyperspectral Imaging Technology with Extreme Learning Machine. Sensors 2019, 19, 4355.

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