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, b...

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Bibliographic Details
Published in2022 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
LanguageEnglish
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
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Summary: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.
ISSN:2158-6276
DOI:10.1109/WHISPERS56178.2022.9955068