Previsão da fertilidade do solo por meio de aprendizado de máquina na província do Alto Amazonas, Peru
DOI:
https://doi.org/10.56926/repia.v3i2.63Palavras-chave:
Random Forest, solos amazônicos, modelagem de solos, solos ácidosResumo
O objetivo do trabalho foi prever a fertilidade do solo na província do Alto Amazonas usando imagens de satélite e técnicas de aprendizado de máquina. O estudo foi realizado na província do Alto Amazonas, no Peru. A amostragem do solo foi realizada em toda a província, totalizando 100 amostras. Posteriormente, foram realizadas análises físicas (textura) e químicas do solo. Imagens de satélite foram obtidas do USGS e os índices de vegetação foram calculados com base nessas imagens. Por fim, a análise descritiva e a modelagem de aprendizado de máquina foram usadas usando 06 algoritmos (GLM, CUBIST, KKNN, SVM, Random Forest e NN) que foram selecionados com base em seu R2 e RMSE. Neste trabalho, observamos que a maioria dos solos da província tem baixo pH, P, Mg, K e alta acidez. Também foram obtidas boas previsões para pH, Ca, Mg e CEC, e observou-se que o algoritmo mais bem-sucedido foi o Random Forest. No entanto, para o Al, o Cubist apresentou melhores resultados. Este é um dos primeiros trabalhos que utilizam o aprendizado de máquina para prever a fertilidade do solo na Amazônia peruana e espera-se que ele possa servir de base para projetos futuros.
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