EDXRF and Machine Learning for Predicting Soil Fertility Attributes

EDXRF and Machine Learning for Predicting Soil Fertility Attributes

Authors

DOI:

https://doi.org/10.5433/1679-0375.2024.v45.51475

Keywords:

Soil fertility attributes, machine learning, PLS, EDXRF

Abstract

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.

Downloads

Download data is not yet available.

Author Biographies

José Vinícius Ribeiro, Universidade Estadual de Londrina

Applied Nuclear Physics Laboratory, UEL, Londrina, PR, Brazil.

Felipe Rodrigues dos Santos, Universidade Estadual de Londrina

Applied Nuclear Physics Laboratory, UEL, Londrina, PR, Brazil.

José Vitor de Oliveira Alves, Universidade Estadual de Londrina

Applied Nuclear Physics Laboratory, UEL, Londrina, PR, Brazil.

Mariana Spinardi Fossaluza, Universidade Estadual de Londrina

Applied Nuclear Physics Laboratory, UEL, Londrina, PR, Brazil.

Igor Marques Nogueira, Universidade Estadual de Londrina

Applied Nuclear Physics Laboratory, UEL, Londrina, PR, Brazil.

José Francirlei de Oliveira, Instituto de Desenvolvimento Rural do Paraná

Soil Department, IDR-Paraná, Londrina, PR, Brazil.

Graziela M. C. Barbosa, Instituto de Desenvolvimento Rural do Paraná

Soil Department, IDR-Paraná, Londrina, PR, Brazil.

Marcelo Marques Lopes Müller, Universidade Estadual do Centro-Oeste

Soil Science and Plant Nutrition Laboratory, UNICENTRO, Guarapuava, PR, Brazil

Renata Alesandra Borecki, Universidade Estadual do Centro-Oeste

Soil Science and Plant Nutrition Laboratory, UNICENTRO, Guarapuava, PR, Brazil

Cristiano Andre Pott, Universidade Estadual do Centro-Oeste

Soil Science and Plant Nutrition Laboratory, UNICENTRO, Guarapuava, PR, Brazil.

Fábio Luiz Melquiades, Universidade Estadual de Londrina

Applied Nuclear Physics Laboratory, UEL, Londrina, PR, Brazil.

References

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

Downloads

Published

2024-11-28

How to Cite

Ribeiro, J. V., dos Santos, F. R., Alves, J. V. de O., Fossaluza, M. S., Nogueira, I. M., de Oliveira, J. F., … Melquiades, F. L. (2024). EDXRF and Machine Learning for Predicting Soil Fertility Attributes. Semina: Ciências Exatas E Tecnológicas, 45, e51475. https://doi.org/10.5433/1679-0375.2024.v45.51475

Issue

Section

Physics

Funding data

Most read articles by the same author(s)

Loading...