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Sensors 2017, 17(10), 2252; https://doi.org/10.3390/s17102252

Hyperspectral Imaging Analysis for the Classification of Soil Types and the Determination of Soil Total Nitrogen

1
College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China
2
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310014, China
*
Author to whom correspondence should be addressed.
Received: 4 September 2017 / Revised: 27 September 2017 / Accepted: 28 September 2017 / Published: 30 September 2017
(This article belongs to the Special Issue Sensors in Agriculture)
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Abstract

Soil is an important environment for crop growth. Quick and accurately access to soil nutrient content information is a prerequisite for scientific fertilization. In this work, hyperspectral imaging (HSI) technology was applied for the classification of soil types and the measurement of soil total nitrogen (TN) content. A total of 183 soil samples collected from Shangyu City (People’s Republic of China), were scanned by a near-infrared hyperspectral imaging system with a wavelength range of 874–1734 nm. The soil samples belonged to three major soil types typical of this area, including paddy soil, red soil and seashore saline soil. The successive projections algorithm (SPA) method was utilized to select effective wavelengths from the full spectrum. Pattern texture features (energy, contrast, homogeneity and entropy) were extracted from the gray-scale images at the effective wavelengths. The support vector machines (SVM) and partial least squares regression (PLSR) methods were used to establish classification and prediction models, respectively. The results showed that by using the combined data sets of effective wavelengths and texture features for modelling an optimal correct classification rate of 91.8%. could be achieved. The soil samples were first classified, then the local models were established for soil TN according to soil types, which achieved better prediction results than the general models. The overall results indicated that hyperspectral imaging technology could be used for soil type classification and soil TN determination, and data fusion combining spectral and image texture information showed advantages for the classification of soil types. View Full-Text
Keywords: hyperspectral imaging; soil type classification; total nitrogen; texture features; data fusion hyperspectral imaging; soil type classification; total nitrogen; texture features; data fusion
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Jia, S.; Li, H.; Wang, Y.; Tong, R.; Li, Q. Hyperspectral Imaging Analysis for the Classification of Soil Types and the Determination of Soil Total Nitrogen. Sensors 2017, 17, 2252.

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