Supporting soil and land assessment with machine learning models using the Vis-NIR spectral response

Gruszczyński, S.; Gruszczyński, Wojciech

doi:10.1016/j.geoderma.2021.115451

Cited by 35 publications

(10 citation statements)

References 34 publications

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“…The number of outputs corresponded to the number of modeled properties. The spectra were standard normal variate (SNV)-transformed [ 34 ] and, like the outputs, were normalized to the numerical interval <0; 1> according to the methodology described in Gruszczyński and Gruszczyński [ 35 ]. The calculations associated with this algorithm were performed in the Python language environment using the Keras system and TensorFlow libraries [ 36 , 37 ].…”

Section: Methodsmentioning

confidence: 99%

Assessing the Information Potential of MIR Spectral Signatures for Prediction of Multiple Soil Properties Based on Data from the AfSIS Phase I Project

Gruszczyński

2022

IJERPH

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The aim of the study was to assess the predictive potential of mid-infrared (MIR) spectral response in the estimation of 60 soil properties. It is important to know the accuracy limitations in estimating various soil characteristics using various models in conditions of high spatial variability of the environment. To fully assess this potential, three types of algorithms were used in modeling, i.e., partial least squares (PLSR), one-dimensional convolutional neural network (1DCNN), and generalized regression neural network (GRNN). The research used data from 19 sub-Saharan African countries collected as part of the Africa Soil Information Service (AfSIS) Phase I project. The repositories provide 18,250 MIR reflectance recordings and nearly two thousand analytical data records from the determination of many soil properties by reference methods. The modeled subset of these properties included texture (three variables), bulk density, moisture content at soil water characteristic curves (SWCC, 4 variables), total and organic C and total N content (3 variables), total elemental content (32 variables), elemental content in bioavailable forms (12 variables), electrical conductivity, exchangeable acidity, exchangeable bases, pH, and phosphorus sorption index. It is not possible to indicate a universal optimal prediction model for all soil variables. The best prediction results are provided by all regression models for total and organic C, total Fe, total Al and bioavailable Al content, and pH. For bulk density, total N and total K content satisfactory results are provided by specific model type. Many other properties, i.e., texture, SWCC, total Ga, Rb, Na, Ca, Cu, Pb, Hg content, and bioavailable Ca and K content, can be predicted with accuracies sufficient for some less demanding tasks.

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Section: Methodsmentioning

confidence: 99%

Assessing the Information Potential of MIR Spectral Signatures for Prediction of Multiple Soil Properties Based on Data from the AfSIS Phase I Project

Gruszczyński

2022

IJERPH

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“…As a result, the SAE divides the multilayer SAE learning into two phases: single-layer AE learning and multilayer AE fine-tuning. Wojciech et al at the AGH University of Science and Technology employed three different models to evaluate soil and land: an SAE, a CNN, and the stack model, which consists of a collection of multilayer perceptron algorithms with two distinct methods for regression estimation that analyze the Vis-NIR spectral response signal [123]. Fu et al proposed a stacked sparse autoencoder combined with a cuckoo search (CS)-optimized-support vector machine (SSAE-CS-SVM) for analyzing the near-infrared (NIR) hyperspectral data of maize seeds (871.61-1766.32 nm) to achieve maize seed variety identification [124].…”

Section: Stacked Autoencoder (Sae) and Variational Autoencoder (Vae)mentioning

confidence: 99%

A Review of Machine Learning for Near-Infrared Spectroscopy

Zhang

Kasun

Wang

et al. 2022

Sensors

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The analysis of infrared spectroscopy of substances is a non-invasive measurement technique that can be used in analytics. Although the main objective of this study is to provide a review of machine learning (ML) algorithms that have been reported for analyzing near-infrared (NIR) spectroscopy from traditional machine learning methods to deep network architectures, we also provide different NIR measurement modes, instruments, signal preprocessing methods, etc. Firstly, four different measurement modes available in NIR are reviewed, different types of NIR instruments are compared, and a summary of NIR data analysis methods is provided. Secondly, the public NIR spectroscopy datasets are briefly discussed, with links provided. Thirdly, the widely used data preprocessing and feature selection algorithms that have been reported for NIR spectroscopy are presented. Then, the majority of the traditional machine learning methods and deep network architectures that are commonly employed are covered. Finally, we conclude that developing the integration of a variety of machine learning algorithms in an efficient and lightweight manner is a significant future research direction.

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“…Kong et al used a partial least squares method in combination with a variety of spectral absorption characteristics to construct an estimation model for soil organic carbon in the European Land Use/Cover Area frame Survey (LUCAS) [24]. Gruszczyńskiet al used CNN model to estimate K element in soil [25]. He.…”

Section: B Problem Statementmentioning

confidence: 99%

Application of Hyperspectral Technology Combined With Bat Algorithm-AdaBoost Model in Field Soil Nutrient Prediction

et al. 2022

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This paper proposes a hyperspectral soil nutrient estimation method based on the bat algorithm (BA)-AdaBoost model. The spectral reflectance, the first derivative of the reflectance, and the reciprocal logarithm of the reflectance are analyzed based on the 800 field soil samples and their hyperspectral data collected. The first derivative of the reciprocal logarithm of the reflectance and the sensitive band was extracted using the correlation coefficient method, and the correlation of the content of soil organic matter, phosphorus, and potassium was solved. The BA is used to optimize the two core parameters of the AdaBoost model (i.e., the maximum number of iterations (n) and the weight reduction coefficient (v) of the weak learner), the classification and regression trees(CART) decision tree is selected as the weak regression learner of the model, and the coefficient of determination is used as parameter optimization. Based on the objective function value, a BA-AdaBoost model was constructed to estimate soil organic matter and phosphorus and potassium contents. The results show that the BA-AdaBoost combined model can better search for globally optimal parameters. The AdaBoost model optimized by BA significantly improved accuracy and reliability. Among the three elements, soil organic matter estimation accuracy is the highest, and the coefficient of determination and the root mean square error are 0.867 and 0.151g • kg -1 , respectively. Compared with the model before optimization, the model accuracy and reliability improved by 29.0% and 24.1%, respectively. The results indicate that hyperspectral technology combined with the BA-AdaBoost model has certain application prospects in field soil nutrient estimation.

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Supporting soil and land assessment with machine learning models using the Vis-NIR spectral response

Cited by 35 publications

References 34 publications

Assessing the Information Potential of MIR Spectral Signatures for Prediction of Multiple Soil Properties Based on Data from the AfSIS Phase I Project

Assessing the Information Potential of MIR Spectral Signatures for Prediction of Multiple Soil Properties Based on Data from the AfSIS Phase I Project

A Review of Machine Learning for Near-Infrared Spectroscopy

Application of Hyperspectral Technology Combined With Bat Algorithm-AdaBoost Model in Field Soil Nutrient Prediction

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