Machine learning algorithms for prediction of physicochemical soil properties by spectral information: a systematic review
The prediction of soil properties through spectral information is widely discussed in the current scientific literature. The objective of this review was to find algorithms with the highest predictive potential for soil physicochemical properties based on spectral information captured with different...
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| Format: | Online |
| Language: | spa |
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Universidad Pedagógica y Tecnológica de Colombia
2022
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| Online Access: | https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/14212 |
| _version_ | 1801706007161733120 |
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| author | Vargas-Zapata, Mateo Medina-Sierra, Marisol Galeano-Vasco, Luis Fernando Cerón-Muñoz, Mario Fernando |
| author_facet | Vargas-Zapata, Mateo Medina-Sierra, Marisol Galeano-Vasco, Luis Fernando Cerón-Muñoz, Mario Fernando |
| author_sort | Vargas-Zapata, Mateo |
| collection | OJS |
| description | The prediction of soil properties through spectral information is widely discussed in the current scientific literature. The objective of this review was to find algorithms with the highest predictive potential for soil physicochemical properties based on spectral information captured with different instruments. A systematic review was carried out in which 121 articles were found, and 19 of them were chosen which met a determination coefficient greater than 0.80 or a root mean square error close to 0. It was determined that the most used spectral range corresponds to the range from 350 to 2500 nm; the partial least squares, support vector machine, and adjusted support vector machine algorithms are suitable for predicting pH, organic matter, and organic carbon. Furthermore, linear regression is only effective in predicting calcium carbonate, organic matter, moisture, and water content using individual bands. |
| format | Online |
| id | oai:oai.revistas.uptc.edu.co:article-14212 |
| institution | Revista de Investigación, Desarrollo e Innovación (RIDI) |
| language | spa |
| publishDate | 2022 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | ojs |
| spelling | oai:oai.revistas.uptc.edu.co:article-142122023-01-31T00:22:35Z Machine learning algorithms for prediction of physicochemical soil properties by spectral information: a systematic review Algoritmos de aprendizaje de máquina para la predicción de propiedades fisicoquímicas del suelo mediante información espectral: una revisión sistemática Vargas-Zapata, Mateo Medina-Sierra, Marisol Galeano-Vasco, Luis Fernando Cerón-Muñoz, Mario Fernando prediction algorithms machine learning chemical analysis spectroscopy algoritmos de predicción aprendizaje de máquina análisis químico espectroscopía The prediction of soil properties through spectral information is widely discussed in the current scientific literature. The objective of this review was to find algorithms with the highest predictive potential for soil physicochemical properties based on spectral information captured with different instruments. A systematic review was carried out in which 121 articles were found, and 19 of them were chosen which met a determination coefficient greater than 0.80 or a root mean square error close to 0. It was determined that the most used spectral range corresponds to the range from 350 to 2500 nm; the partial least squares, support vector machine, and adjusted support vector machine algorithms are suitable for predicting pH, organic matter, and organic carbon. Furthermore, linear regression is only effective in predicting calcium carbonate, organic matter, moisture, and water content using individual bands. En la literatura científica actual se discute ampliamente acerca de la predicción de propiedades edáficas mediante información espectral. El objetivo de esta revisión fue encontrar algoritmos con el mayor potencial predictivo para las propiedades fisicoquímicas del suelo, basados en información espectral capturada con diferentes instrumentos. Se realizó una revisión sistemática en la cual se encontraron 121 artículos de los cuales se eligieron 19, que cumplieran con un coeficiente de determinación mayor a 0,80 o una raíz del error cuadrado medio cercana a 0. Se determinó que el rango espectral más utilizado corresponde al rango desde 350 hasta 2500 nm; los algoritmos mínimos cuadrados parciales, máquina de soporte vectorial y máquina de soporte vectorial ajustado son adecuadas para predecir pH, materia orgánica y carbono orgánico. Además, la regresión lineal solo es efectiva para predecir el carbonato de calcio, materia orgánica, humedad y contenido de agua mediante bandas individuales. Universidad Pedagógica y Tecnológica de Colombia 2022-02-15 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf text/xml https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/14212 10.19053/20278306.v12.n1.2022.14212 Revista de Investigación, Desarrollo e Innovación; Vol. 12 No. 1 (2022): Enero-Junio; 107-120 Revista de Investigación, Desarrollo e Innovación; Vol. 12 Núm. 1 (2022): Enero-Junio; 107-120 2389-9417 2027-8306 spa https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/14212/11646 https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/14212/12562 Derechos de autor 2022 Revista de Investigación, Desarrollo e Innovación |
| spellingShingle | prediction algorithms machine learning chemical analysis spectroscopy algoritmos de predicción aprendizaje de máquina análisis químico espectroscopía Vargas-Zapata, Mateo Medina-Sierra, Marisol Galeano-Vasco, Luis Fernando Cerón-Muñoz, Mario Fernando Machine learning algorithms for prediction of physicochemical soil properties by spectral information: a systematic review |
| title | Machine learning algorithms for prediction of physicochemical soil properties by spectral information: a systematic review |
| title_alt | Algoritmos de aprendizaje de máquina para la predicción de propiedades fisicoquímicas del suelo mediante información espectral: una revisión sistemática |
| title_full | Machine learning algorithms for prediction of physicochemical soil properties by spectral information: a systematic review |
| title_fullStr | Machine learning algorithms for prediction of physicochemical soil properties by spectral information: a systematic review |
| title_full_unstemmed | Machine learning algorithms for prediction of physicochemical soil properties by spectral information: a systematic review |
| title_short | Machine learning algorithms for prediction of physicochemical soil properties by spectral information: a systematic review |
| title_sort | machine learning algorithms for prediction of physicochemical soil properties by spectral information a systematic review |
| topic | prediction algorithms machine learning chemical analysis spectroscopy algoritmos de predicción aprendizaje de máquina análisis químico espectroscopía |
| topic_facet | prediction algorithms machine learning chemical analysis spectroscopy algoritmos de predicción aprendizaje de máquina análisis químico espectroscopía |
| url | https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/14212 |
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