Prediction of soil fertility using machine learning in Alto Amazonas province, Peru
DOI:
https://doi.org/10.56926/repia.v3i2.63Keywords:
Random Forest, amazon soils, soil modelling, acid soilsAbstract
The objective of the work was to predict soil fertility in the province of Alto Amazonas with the use of satellite images and machine learning techniques. The study was in the province of Alto Amazonas in Peru. Soil sampling was carried out in all the provinces, totalling 100 samples. Afterwards, soil physical (texture) and chemical analyses were performed. Satellite images were obtained from USGS, and vegetation indexes were calculated based on these images. Finally, descriptive analysis and machine learning modelling using 06 algorithms (GLM, CUBIST, KKNN, SVM, Random Forest and NN) were used and selected based on their R2 and rmse. In this work, we observed that most soils in the province have low pH, P, Mg, K and high acidity. We also managed to achieve good predictions for pH, Ca, Mg and CEC, and we observed that the most successful algorithm was Random Forest. Nevertheless, for Al, CUBIST performed better. This is one of the first works using machine learning to predict soil fertility in the Peruvian Amazon, and we hope it may serve as a base for future projects.
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Copyright (c) 2024 César Oswaldo Arévalo-Hernández, Enrique Arévalo-Gardini, Luis Alberto Arévalo-López, Oscar Tuesta-Hidalgo, Dayani Shirley Romero-Vela, Claudia Elizabeth Ruiz-Camus
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