EDXRF and Machine Learning for Predicting Soil Fertility Attributes
DOI:
https://doi.org/10.5433/1679-0375.2024.v45.51475Keywords:
Soil fertility attributes, machine learning, PLS, EDXRFAbstract
Soil fertility evaluation is fundamental for sustainable agricultural practices, often relying on conventional laboratory methods. These methods, while accurate, are labor-intensive, time-consuming, and require chemical reagents. Spectroscopic sensors, such as energy-dispersive X-ray fluorescence (EDXRF), offer a rapid and non-destructive alternative but require calibration of machine learning models for accurate prediction of fertility attributes. In this context, this study compares the performance of four machine learning algorithms—multiple linear regression (MLR), partial least square regression (PLS), support vector machine regression (SVM), and random forest regression (RF)—in predicting soil pH, organic carbon (SOC), sum of exchangeable bases (BS), and cation exchange capacity (CEC) using EDXRF data from two soil datasets. Results indicate that PLS models outperformed others (the hierarchy of accuracy was PLS > MLR > SVM > RF). Overall, we emphasize the benefits of integrating PLS with EDXRF, capable of mitigating the use of traditional soil analysis.
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Biau, G., & Scornet, E. (2016). A random forest guided tour. TEST, 25(2), 197–227. DOI: https://doi.org/10.1007/s11749-016-0481-7
da Silva, F. C. (2009). Manual de análises químicas de solos, plantas e fertilizantes. Brasília, DF: Embrapa Informação Tecnológica; Rio de Janeiro: Embrapa Solos.
de Ciência do Solo. Núcleo Regional Sul, S. B. (2004). Manual de adubação e de calagem: para os estados do Rio Grande do Sul e de Santa Catarina. Comissão de Química e Fertilidade do Solo-RS/SC.
de Santana, F. B., de Souza, A. M., & Poppi, R. J. (2018). Visible and near infrared spectroscopy coupled to random forest to quantify some soil quality parameters. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 191, 454–462. DOI: https://doi.org/10.1016/j.saa.2017.10.052
de Santana, F. B., Otani, S. K., de Souza, A. M., & Poppi, R. J. (2021). Comparison of PLS and SVM models for soil organic matter and particle size using vis-NIR spectral libraries. Geoderma Regional, 27, e00436. DOI: https://doi.org/10.1016/j.geodrs.2021.e00436
Demattê, J. A. M., Dotto, A. C., Bedin, L. G., Sayão, V. M., & Souza, A. B. e. (2019). Soil analytical quality control by traditional and spectroscopy techniques: Constructing the future of a hybrid laboratory for low environmental impact. Geoderma, 337, 111–121. DOI: https://doi.org/10.1016/j.geoderma.2018.09.010
dos Santos, F. R., de Oliveira, J. F., Barbosa, G. M. C., & Melquiades, F. L. (2021). Comparison between energy dispersive X-ray fluorescence spectral data and elemental data for soil attributes modelling. Spectrochimica Acta Part B: Atomic Spectroscopy, 185, 106303. DOI: https://doi.org/10.1016/j.sab.2021.106303
dos Santos, F. R., de Oliveira, J. F., Bona, E., Barbosa, G. M. C., & Melquiades, F. L. (2021). Evaluation of pre-processing and variable selection on energy dispersive X-ray fluorescence spectral data with partial least square regression: A case of study for soil organic carbon prediction. Spectrochimica Acta Part B: Atomic Spectroscopy, 175, 106016. DOI: https://doi.org/10.1016/j.sab.2020.106016
dos Santos, F. R., de Oliveira, J. F., Bona, E., Barbosa, G. M. C., & Melquiades, F. L. (2023). Data fusion of XRF and vis-NIR using p-ComDim to predict some fertility attributes in tropical soils derived from basalt. Microchemical Journal, 191, 108813. DOI: https://doi.org/10.1016/j.microc.2023.108813
dos Santos, F. R., de Oliveira, J. F., Bona, E., dos Santos, J. V. F., Barboza, G. M. C., & Melquiades, F. L. (2020). EDXRF spectral data combined with PLSR to determine some soil fertility indicators. Microchemical Journal, 152, 104275. DOI: https://doi.org/10.1016/j.microc.2019.104275
Eitelwein, M. T., Tavares, T. R., Molin, J. P., Trevisan, R. G., de Sousa, R. V., & Demattê, J. A. M. (2022). Predictive Performance of Mobile Vis–NIR Spectroscopy for Mapping Key Fertility Attributes in Tropical Soils through Local Models Using PLS and ANN. Automation, 3(1), 116–131. DOI: https://doi.org/10.3390/automation3010006
Filgueiras, P. R., Terra, L. A., Castro, E. V. R., Oliveira, L. M. S. L., Dias, J. C. M., & Poppi, R. J. (2015). Prediction of the distillation temperatures of crude oils using 1H NMR and support vector regression with estimated confidence intervals. Talanta, 142, 197–205. DOI: https://doi.org/10.1016/j.talanta.2015.04.046
Fontenelli, J. V., Adamchuk, V. I., Ferreira, M. M. C., Amaral, L. R., Guimarães, C. C. B., Demattê, J. A. M., & Magalhães, P. S. G. (2021). Evaluating the synergy of three soil spectrometers for improving the prediction and mapping of soil properties in a high anthropic management area: A case of study from Southeast Brazil. Geoderma, 402, 115347. DOI: https://doi.org/10.1016/j.geoderma.2021.115347
Garthwaite, P. H. (1994). An Interpretation of Partial Least Squares. Journal of the American Statistical Association, 89(425), 122–127. DOI: https://doi.org/10.1080/01621459.1994.10476452
Geladi, P., & Kowalski, B. R. (1986). Partial least-squares regression: a tutorial. Analytica Chimica Acta, 185, 1–17. DOI: https://doi.org/10.1016/0003-2670(86)80028-9
Jobson, J. D. (1991). Applied Multivariate Data Analysis. Springer New York. DOI: https://doi.org/10.1007/978-1-4612-0955-3
Kennard, R. W., & Stone, L. A. (1969). Computer Aided Design of Experiments. Technometrics, 11(1), 137–148. DOI: https://doi.org/10.1080/00401706.1969.10490666
Kucheryavskiy, S. (2020). mdatools — R package for chemometrics. Chemometrics and Intelligent Laboratory Systems, 198. DOI: https://doi.org/10.1016/j.chemolab.2020.103937
Mateos-Aparicio, G. (2011). Partial Least Squares (PLS) Methods: Origins, Evolution, and Application to Social Sciences. Communications in Statistics - Theory and Methods, 40(13), 2305–2317. DOI: https://doi.org/10.1080/03610921003778225
Mauad, M., Grassi Filho, H., Crusciol, C. A. C., & Corrêa, J. C. (2003). Teores de silício no solo e na planta de arroz de terras altas com diferentes doses de adubação silicatada e nitrogenada. Revista Brasileira de Ciência Do Solo, 27(5), 867–873. DOI: https://doi.org/10.1590/S0100-06832003000500011
Melquiades, F. L., & dos Santos, F. R. (2015). Preliminary Results: Energy Dispersive X-Ray Fluorescence and Partial Least Squares Regression for Organic Matter Determination in Soil. Spectroscopy Letters, 48(4), 286–289. DOI: https://doi.org/10.1080/00387010.2013.874532
Morona, F., dos Santos, F. R., Brinatti, A. M., & Melquiades, F. L. (2017). Quick analysis of organic matter in soil by energy-dispersive X-ray fluorescence and multivariate analysis. Applied Radiation and Isotopes, 130, 13–20. DOI: https://doi.org/10.1016/j.apradiso.2017.09.008
Nawar, S., & Mouazen, A. M. (2018). Optimal sample selection for measurement of soil organic carbon using on-line vis-NIR spectroscopy. Computers and Electronics in Agriculture, 151, 469–477. DOI: https://doi.org/10.1016/j.compag.2018.06.042
Nawar, S., Richard, F., Kassim, A. M., Tekin, Y., & Mouazen, A. M. (2022). Fusion of Gamma-rays and portable X-ray fluorescence spectral data to measure extractable potassium in soils. Soil and Tillage Research, 223, 105472. DOI: https://doi.org/10.1016/j.still.2022.105472
Oshunsanya, S. O., Oluwasemire, K. O., & Taiwo, O. J. (2017). Use of GIS to Delineate Site-Specific Management Zone for Precision Agriculture. Communications in Soil Science and Plant Analysis, 48(5), 565–575. DOI: https://doi.org/10.1080/00103624.2016.1270298
Pavinato, P. S., Pauletti, V., Motta, A. C. V., Moreira, A., & Motta, A. C. V. (2017). Manual de adubação e calagem para o Estado do Paraná. Sociedade Brasileira de Ciência do Solo (SBCS). Núcleo Estadual do Paraná (NEPAR).
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel V. and Thirion, B., Grisel, O., Blondel, M., Prettenhofer P. and Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
Pramod Pawase, P., Madhukar Nalawade, S., Balasaheb Bhanage, G., Ashok Walunj, A., Bhaskar Kadam, P., G Durgude, A., & R Patil, M. (2023). Variable rate fertilizer application technology for nutrient management: A review. International Journal of Agricultural and Biological Engineering, 16(4), 11–19. DOI: https://doi.org/10.25165/j.ijabe.20231604.7671
Prezotti, L. C., & Guarçoni, A. M. (2013). Guia de interpretação de análise de solo e foliar. Incaper.
R Core Team. (2024). R: A Language and Environment for Statistical Computing. https://www.R-project.org/
Ribeiro, J. V., dos Santos, F. R., de Oliveira, J. F., Barbosa, G. M. C., & Melquiades, F. L. (2024). Optimization of pXRF instrumentation conditions and multivariate modeling in soil fertility attributes determination. Spectrochimica Acta Part B: Atomic Spectroscopy, 211, 106835. DOI: https://doi.org/10.1016/j.sab.2023.106835
Ronquim, C. C. (2010). Conceitos de fertilidade do solo e manejo adequado para as regiões tropicais.
Sharma, A., Weindorf, D. C., Man, T., Aldabaa, A. A. A., & Chakraborty, S. (2014). Characterizing soils via portable X-ray fluorescence spectrometer: 3. Soil reaction (pH). Geoderma, 232–234, 141–147. DOI: https://doi.org/10.1016/j.geoderma.2014.05.005
Stevens, A., & Ramirez-Lopez, L. (2024). An introduction to the prospectr package (0.2.7). R package Vignette. https://cran.r-project.org/web/packages/prospectr/index.html
Tavares, T. R., Minasny, B., McBratney, A., Molin, J. P., Marques, G. T., Ragagnin, M. M., dos Santos, F. R., de Carvalho, H. W. P., & Lavres, J. (2025). Do XRF local models have temporal stability for predicting plant-available nutrients in different years? A long-term study showing the effect of soil fertility management in a tropical field. Soil and Tillage Research, 245, 106307. DOI: https://doi.org/10.1016/j.still.2024.106307
Tavares, T. R., Molin, J. P., Nunes, L. C., Wei, M. C. F., Krug, F. J., de Carvalho, H. W. P., & Mouazen, A. M. (2021). Multi-Sensor Approach for Tropical Soil Fertility Analysis: Comparison of Individual and Combined Performance of VNIR, XRF, and LIBS Spectroscopies. Agronomy, 11(6), 1028. DOI: https://doi.org/10.3390/agronomy11061028
Terra, J., Sanches, R. O., Bueno, M. I. M. S., & Melquiades, F. L. (2014). Análise Multielementar de solos: uma proposta envolvendo equipamento portátil de fluorescência de raios X. Semina: Ciências Exatas e Tecnológicas, 35(2), 207. DOI: https://doi.org/10.5433/1679-0375.2014v35n2p207
Van Grieken, R. E., & Markowicz, A. A. (2001). Handbook of X-ray spectrometry (Marcel Dekker, Ed.; 2nd ed., Vol. 29). DOI: https://doi.org/10.1201/9780203908709
Viscarra Rossel, R. A., McGlynn, R. N., & McBratney, A. B. (2006). Determining the composition of mineral-organic mixes using UV–vis–NIR diffuse reflectance spectroscopy. Geoderma, 137(1–2), 70–82. DOI: https://doi.org/10.1016/j.geoderma.2006.07.004
Zhang, F., & O’Donnell, L. J. (2020). Support vector regression. In Machine Learning (pp. 123–140). Elsevier. DOI: https://doi.org/10.1016/B978-0-12-815739-8.00007-9
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Copyright (c) 2024 José Vinícius Ribeiro, Felipe Rodrigues dos Santos, José Vitor de Oliveira Alves, Mariana Spinardi Fossaluza, Igor Marques Nogueira, José Francirlei de Oliveira, Graziela M. C. Barbosa, Marcelo Marques Lopes Müller, Renata Alesandra Borecki, Cristiano Andre Pott, Fábio Luiz Melquiades
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Conselho Nacional de Desenvolvimento Científico e Tecnológico
Grant numbers 306309/2023-8