Machine-Learning model for estimating sugarcane production at crop level

Abstract

Yield maps provide essential information for those who manage the field. The anticipated production data will be able to make better decisions on how resources should be used in harvesting, define market strategies and, above all, it will help evaluate treatments used on the crop. Sugar cane is the predominant crop in Huasteca Potosina, Mexico. The proposed Machine Learning model based on Random Forest Regressor integrates time series of vegetation indices extracted from Sentinel-2 images and meteorological data. The R2 and RMSE metrics (0.84 y 0.034) show the effectiveness of the model for prediction.