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

• Published in: 2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) pp. 1 - 5
• Main Authors: Diaz-Gonzalez, Freddy A., Correa-Florez, Carlos A., Vuelvas, Jose, Vallejo, Victoria E., Patino, D.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 13.09.2022
• Subjects: Agricultural Systems; Analytical models; Autonomous aerial vehicles; Machine learning; Maximum likelihood estimation; Remote Sensing; Signal processing algorithms; Soil; Soil Quality Indicators; UAV
• DOI: 10.1109/WHISPERS56178.2022.9955068

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.