Machine Learning for Soil Attribute Prediction: An Effectiveness and Dimensionality Reduction Analysis

• José Solenir L. Figuerêdo UEFS
• Marcos Eduardo de C. Ferreira UEFS
• Rodrigo T. Calumby UEFS

DOI: https://doi.org/10.5753/sbiagro.2023.26572

Resumo

Traditional soil fertility analyzes are laborious, expensive, timeconsuming and produce hazardous waste. Although many works using machine learning (ML) has been done to address these issues, some algorithms and dimensionality reduction strategies require further investigation. Therefore, in this study we evaluated the potential of Support Vector Regression and Ridge regression in determining soil attributes, and compared principal components regression and partial least squares regression (PLSR). The results showed that Ridge was the most effective model. In addition, our experiments revealed that PLSR was able to achieve statistically equivalent results, and in some cases superior to the baseline, but using a much smaller average number of components.

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