A methodology for estimating soil quality indicators in agricultural systems using UAV and machine learning

The farmers require methodologies to estimate soil quality indicators (SQI) using low-cost technologies for data collection and processing, combined with traditional soil quality assessment tools. Therefore, this work presents a methodology to estimate SQI in agricultural systems at a local scale, based machine learning (ML) regression models to process georeferenced-multimencional database. The results of the regressions of the analyzed SQI presented are consistent with literature, as to establish a SQIs estimation model based on ML algorithms.