Prediction of soil fertility properties in Southern Brazil via proximal sensing

Abstract Although proximal sensing coupled with machine learning (ML) algorithms have been successful for characterizing soils, questions remain regarding their effectiveness under varied soil conditions. This study evaluated for the first time the efficiency of a portable X-ray fluorescence spectro...

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Published in:Anais da Academia Brasileira de Ciências Vol. 97; no. suppl 2; p. e20250075
Main Authors: NACHTIGALL, STEFAN D., MANCINI, MARCELO, REIS, RENATA A., ARAÚJO, ELIAS FRANK DE, CARNEIRO, MARCO AURÉLIO C., CURI, NILTON, SILVA, SÉRGIO HENRIQUE GODINHO
Format: Journal Article
Language:English
Published: Brazil Academia Brasileira de Ciências 01.01.2025
Subjects:
Algorithms
Brazil
Environmental Monitoring - methods
Machine Learning
MULTIDISCIPLINARY SCIENCES
pedometrics
Portable X-ray fluorescence
Soil - chemistry
soil properties
soil variation
Spectrometry, X-Ray Emission - methods
Brazil
soil properties
Portable X-ray fluorescence
pedometrics
soil variation
ISSN:0001-3765, 1678-2690, 1678-2690
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Abstract Abstract Although proximal sensing coupled with machine learning (ML) algorithms have been successful for characterizing soils, questions remain regarding their effectiveness under varied soil conditions. This study evaluated for the first time the efficiency of a portable X-ray fluorescence spectrometer (pXRF) to predict 17 soil fertility properties in Rio Grande do Sul (RS) state, Brazil, through ML algorithms. A total of 468 surface soil samples were analyzed by pXRF and by conventional (reference) methods. Six algorithms were employed: Projection Pursuit Regression, Partial Least Squares, Random Forest, Support Vector Machine, Extreme Gradient Boosting, and Cubist. Predictions accuracy was assessed using the coefficient of determination (R²), root mean square error, normalized root mean square error, residual prediction deviation (RPD) and Ratio of Performance to Interquartile Distance. Cubist and Random Forest outperformed other algorithms, reaching the following R² values: available/exchangeable Al (R² = 0.70), Ca (0.57), Mg (0.75), Mn (0.84), S (0.60), Cu (0.81), K (0.82), P (0.54), besides P-rem (0.80), H+Al (0.73), and total N (0.52). Predictions for organic carbon and available B, Fe, Na, Zn require further investigations. The pXRF combined with ML algorithms can accelerate decisions for agricultural management in RS state, Brazil, by optimizing soil analysis for improved crop management.
AbstractList Abstract Although proximal sensing coupled with machine learning (ML) algorithms have been successful for characterizing soils, questions remain regarding their effectiveness under varied soil conditions. This study evaluated for the first time the efficiency of a portable X-ray fluorescence spectrometer (pXRF) to predict 17 soil fertility properties in Rio Grande do Sul (RS) state, Brazil, through ML algorithms. A total of 468 surface soil samples were analyzed by pXRF and by conventional (reference) methods. Six algorithms were employed: Projection Pursuit Regression, Partial Least Squares, Random Forest, Support Vector Machine, Extreme Gradient Boosting, and Cubist. Predictions accuracy was assessed using the coefficient of determination (R²), root mean square error, normalized root mean square error, residual prediction deviation (RPD) and Ratio of Performance to Interquartile Distance. Cubist and Random Forest outperformed other algorithms, reaching the following R² values: available/exchangeable Al (R² = 0.70), Ca (0.57), Mg (0.75), Mn (0.84), S (0.60), Cu (0.81), K (0.82), P (0.54), besides P-rem (0.80), H+Al (0.73), and total N (0.52). Predictions for organic carbon and available B, Fe, Na, Zn require further investigations. The pXRF combined with ML algorithms can accelerate decisions for agricultural management in RS state, Brazil, by optimizing soil analysis for improved crop management.
Although proximal sensing coupled with machine learning (ML) algorithms have been successful for characterizing soils, questions remain regarding their effectiveness under varied soil conditions. This study evaluated for the first time the efficiency of a portable X-ray fluorescence spectrometer (pXRF) to predict 17 soil fertility properties in Rio Grande do Sul (RS) state, Brazil, through ML algorithms. A total of 468 surface soil samples were analyzed by pXRF and by conventional (reference) methods. Six algorithms were employed: Projection Pursuit Regression, Partial Least Squares, Random Forest, Support Vector Machine, Extreme Gradient Boosting, and Cubist. Predictions accuracy was assessed using the coefficient of determination (R²), root mean square error, normalized root mean square error, residual prediction deviation (RPD) and Ratio of Performance to Interquartile Distance. Cubist and Random Forest outperformed other algorithms, reaching the following R² values: available/exchangeable Al (R² = 0.70), Ca (0.57), Mg (0.75), Mn (0.84), S (0.60), Cu (0.81), K (0.82), P (0.54), besides P-rem (0.80), H+Al (0.73), and total N (0.52). Predictions for organic carbon and available B, Fe, Na, Zn require further investigations. The pXRF combined with ML algorithms can accelerate decisions for agricultural management in RS state, Brazil, by optimizing soil analysis for improved crop management.Although proximal sensing coupled with machine learning (ML) algorithms have been successful for characterizing soils, questions remain regarding their effectiveness under varied soil conditions. This study evaluated for the first time the efficiency of a portable X-ray fluorescence spectrometer (pXRF) to predict 17 soil fertility properties in Rio Grande do Sul (RS) state, Brazil, through ML algorithms. A total of 468 surface soil samples were analyzed by pXRF and by conventional (reference) methods. Six algorithms were employed: Projection Pursuit Regression, Partial Least Squares, Random Forest, Support Vector Machine, Extreme Gradient Boosting, and Cubist. Predictions accuracy was assessed using the coefficient of determination (R²), root mean square error, normalized root mean square error, residual prediction deviation (RPD) and Ratio of Performance to Interquartile Distance. Cubist and Random Forest outperformed other algorithms, reaching the following R² values: available/exchangeable Al (R² = 0.70), Ca (0.57), Mg (0.75), Mn (0.84), S (0.60), Cu (0.81), K (0.82), P (0.54), besides P-rem (0.80), H+Al (0.73), and total N (0.52). Predictions for organic carbon and available B, Fe, Na, Zn require further investigations. The pXRF combined with ML algorithms can accelerate decisions for agricultural management in RS state, Brazil, by optimizing soil analysis for improved crop management.
Although proximal sensing coupled with machine learning (ML) algorithms have been successful for characterizing soils, questions remain regarding their effectiveness under varied soil conditions. This study evaluated for the first time the efficiency of a portable X-ray fluorescence spectrometer (pXRF) to predict 17 soil fertility properties in Rio Grande do Sul (RS) state, Brazil, through ML algorithms. A total of 468 surface soil samples were analyzed by pXRF and by conventional (reference) methods. Six algorithms were employed: Projection Pursuit Regression, Partial Least Squares, Random Forest, Support Vector Machine, Extreme Gradient Boosting, and Cubist. Predictions accuracy was assessed using the coefficient of determination (R²), root mean square error, normalized root mean square error, residual prediction deviation (RPD) and Ratio of Performance to Interquartile Distance. Cubist and Random Forest outperformed other algorithms, reaching the following R² values: available/exchangeable Al (R² = 0.70), Ca (0.57), Mg (0.75), Mn (0.84), S (0.60), Cu (0.81), K (0.82), P (0.54), besides P-rem (0.80), H+Al (0.73), and total N (0.52). Predictions for organic carbon and available B, Fe, Na, Zn require further investigations. The pXRF combined with ML algorithms can accelerate decisions for agricultural management in RS state, Brazil, by optimizing soil analysis for improved crop management.
Author REIS, RENATA A.
MANCINI, MARCELO
ARAÚJO, ELIAS FRANK DE
CARNEIRO, MARCO AURÉLIO C.
CURI, NILTON
NACHTIGALL, STEFAN D.
SILVA, SÉRGIO HENRIQUE GODINHO
AuthorAffiliation CMPC Celulose RioGrandense
Universidade Federal de Lavras
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  surname: MANCINI
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  organization: Universidade Federal de Lavras, Brazil
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  surname: SILVA
  fullname: SILVA, SÉRGIO HENRIQUE GODINHO
  organization: Universidade Federal de Lavras, Brazil
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Issue suppl 2
Keywords soil properties
Portable X-ray fluorescence
pedometrics
soil variation
Language English
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Snippet Abstract Although proximal sensing coupled with machine learning (ML) algorithms have been successful for characterizing soils, questions remain regarding...
Although proximal sensing coupled with machine learning (ML) algorithms have been successful for characterizing soils, questions remain regarding their...
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SubjectTerms Algorithms
Brazil
Environmental Monitoring - methods
Machine Learning
MULTIDISCIPLINARY SCIENCES
pedometrics
Portable X-ray fluorescence
Soil - chemistry
soil properties
soil variation
Spectrometry, X-Ray Emission - methods
Title Prediction of soil fertility properties in Southern Brazil via proximal sensing
URI https://www.ncbi.nlm.nih.gov/pubmed/41059799
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