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

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.

Development of a predictive tool for rapid assessment of soil total nitrogen in wheat-corn double cropping system with hyperspectral data

“…The general trend of soil spectral reflectance in the wavelength of 400-2400 nm was similar for wheat season and corn season is shown in Figure 2 with samples signed as different colors. Features of baseline translation and tilt between the spectra, which may be caused by scattering effects of different soil particles [18], occurred at roughly the same bands of wavelength. However, variations among samples at a particular wavelength were generally larger for the wheat season than for the corn season.…”

“…where R 1 and R 2 are consecutive bands in the sequential analysis. After transformation, the characteristic bands of the soil samples were screened based on the combined spectral reflectance absorption rules and collinearity diagnosis [17,18]. The bands with absolute r values larger than 0.55 were firstly selected.…”

Development of Visible/Near-Infrared Hyperspectral Imaging for the Prediction of Total Arsenic Concentration in Soil

“…For each hyperspectral image, a region of interest (ROI) was used to measure the mean VNIR spectral reflectance. The ROI (a circle with a diameter of about 150 pixels) was positioned in the middle of the sample image, and close to Petri dish (90 × 17 mm) edge [20,39] (Figure 2). The spectral bands for this study are 519, 560, 564, 576, 697, 700, 703, 706, and 749 nm.…”

“…The smaller the RMSE and RE, the better the predictive ability. The parameters of the determination coefficients (R 2 ), root-mean-square error (RMSE), and relative error (RE%) were used to measure the accuracy of the models [39,49]. The closer R 2 is to 1, the better the stability of the model and the higher the degree of fit.…”

Classification of Granite Soils and Prediction of Soil Water Content Using Hyperspectral Visible and Near-Infrared Imaging

“…Since the early 2000s, near-infrared (NIR) spectroscopy has been widely employed as a useful tool for the analysis of soil properties [11]. NIR spectroscopy can be used to evaluate the properties of soil that are not disturbed by light [12,13].…”