Estimation of Soil Properties by Remote Sensing and Machine Learning
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Abstract
Soil suitability for a crop can be determined by quality indicators, which can be obtained by different methods. The objective of this research was to estimate the properties of a vertisol dedicated to rice cultivation by means of remote sensing and automatic learning. Chemical and physical properties of the soil that affect rice physiology were determined “in situ”, with a completely randomized sampling of 100 points at a depth of 0 m to 0.20 m. Properties were estimated from the Normalized Difference Vegetation Index (NDVI) using machine learning tools. Organic matter, calcium, magnesium and phosphorus can be estimated by linear regression with NDVI, considering that they had Standard Errors of 0.27, 3.41, 4.12 and 1.68, respectively, and coefficients of determination close to 1. The random forest technique showed the best performance, with values in its determination close to 1 and an error in its estimation and validation close to 0.
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