Pedosphere 35(2): 325--337, 2025
ISSN 1002-0160/CN 32-1315/P
©2025 Soil Science Society of China
Published by Elsevier B.V. and Science Press
| Prediction of lead in agricultural soils: An integrated approach using machine learning, terrain attributes and reflectance spectra |
Prince Chapman AGYEMAN1,2 , Kingsley JOHN3, Ndiye Michael KEBONYE4,5, Vahid KHOSRAVI1, Luboš BORŮVKA1, Radim VAŠÁT1, Charles Mario BOATENG6 |
1 Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague 16500 (Czech Republic)
2 Environment and Sustainability, School of Science and the Environment, Grenfell Campus, Memorial University of Newfoundland, Corner Brook, NL A2H 5G4 (Canada)
3 Department of Plant, Food, and Environmental Sciences, Faculty of Agriculture, Dalhousie University, Cox Institute Building, 50 Pictou Rd., P. O. Box 550, Truro N S B2N 5E3 (Canada)
4 Department of Geosciences, Chair of Soil Science and Geomorphology, University of Tübingen, Rümelinstr. 19-23, Tübingen D-72070 (Germany)
5 Deutsche Forschungsgemeinschaf (DFG) Cluster of Excellence Machine Learning:New Perspectives for Science, University of Tübingen, Artificial Intelligence (AI) Research Building, Maria-von-Linden-Str. 6, Tübingen 72076 (Germany)
6 Department of Marine and Fisheries Sciences, University of Ghana, Legon 233 (Ghana) |
| Corresponding Author:Prince Chapman AGYEMAN |
| ABSTRACT |
| Very few studies have benefited from the synergetic implementation of visible, near-infrared, and shortwave infrared (VNIR-SWIR) spectra and terrain attributes in predicting Pb content in agricultural soils. To fill this gap, this study aimed to predict lead (Pb) contents in agricultural soils by combining machine learning algorithms (MLAs) with VNIR-SWIR spectra or/and terrain attributes under three distinct approaches. Six MLAs were tested, including artificial neural network (ANN), partial least squares regression, support vector machine (SVM), Gaussian process regression (GPR), extreme gradient boosting (EGB), and Cubist. The VNIR-SWIR spectral data were preprocessed by methods of discrete wavelet transformation, logarithmic transformation-Savitzky Golay smoothing, standard normal variate (SNV), multiplicative scatter correction, first derivative (FiD), and second derivative. In approach 1, MLAs were combined with the preprocessed VNIR-SWIR spectral data. The Cubist-FiD combination was the most effective, achieving a coefficient of determination (R2) of 0.63, a concordance correlation coefficient (CCC) of 0.51, a mean absolute error (MAE) of 6.87 mg kg-1, and a root mean square error (RMSE) of 8.66 mg kg-1. In approach 2, MLAs were combined with both preprocessed VNIR-SWIR spectral data and terrain attributes, and the EGB-SNV combination yielded superior results with R2 of 0.75, CCC of 0.65, MAE of 5.48 mg kg-1, and RMSE of 7.34 mg kg-1. Approach 3 combined MLAs and terrain attributes, and Cubist demonstrated the best prediction results, with R2 of 0.75, CCC of 0.66, MAE of 6.18 mg kg-1, and RMSE of 7.71 mg kg-1. The cumulative assessment identified the fusion of terrain properties, SNV-preprocessed VNIR-SWIR spectra, and EGB as the optimal method for estimating Pb content in agricultural soils, yielding the highest R2 value and minimal error. Comparatively, GPR, ANN, and SVM techniques achieved higher R2 values in approaches 2 and 3 but also exhibited higher estimation errors. In conclusion, the study underscores the significance of using relevant auxiliary datasets and appropriate MLAs for accurate Pb content prediction with minimal error in agricultural soils. The findings contribute valuable insights for developing successful soil management strategies based on predictive modeling. |
| Key Words: artificial neural network,extreme gradient boosting,Gaussian process regression,support vector machine,trivariate mapping |
| Citation: Agyeman P C, John K, Kebonye N M, Khosravi V, Borůvka L, Vašát R, Boateng C M. 2025. Prediction of lead in agricultural soils: An integrated approach using machine learning, terrain attributes and reflectance spectra. Pedosphere. 35(2): 325-337. |
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