VARGAS-ZAPATA, Mateo; MEDINA-SIERRA, Marisol; GALEANO-VASCO, Luis Fernando    CERON-MUNOZ, Mario Fernando. Machine learning algorithms for prediction of physicochemical soil properties by spectral information: a systematic review. []. , 12, 1, pp.107-120.   10--2022. ISSN 2027-8306.  https://doi.org/10.19053/20278306.v12.n1.2022.14212.

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.

: prediction algorithms; machine learning; chemical analysis; spectroscopy.

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