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

Freddy A. Diaz-Gonzalez, Carlos A. Correa-Florez, José Vuelvas, Victoria E. Vallejo, D. Patino

Producción: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojada2022 12th Workshop on Hyperspectral Imaging and Signal Processing
Subtítulo de la publicación alojadaEvolution in Remote Sensing, WHISPERS 2022
EditorialIEEE Computer Society
ISBN (versión digital)9781665470698
DOI
EstadoPublicada - 2022
Evento12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2022 - Rome, Italia
Duración: 13 sep. 202216 sep. 2022

Serie de la publicación

NombreWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
Volumen2022-September
ISSN (versión impresa)2158-6276

Conferencia

Conferencia12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2022
País/TerritorioItalia
CiudadRome
Período13/09/2216/09/22

Huella

Profundice en los temas de investigación de 'A methodology for estimating soil quality indicators in agricultural systems using UAV and machine learning'. En conjunto forman una huella única.

Citar esto