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

doi:10.1109/WHISPERS56178.2022.9955068

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

Department of Electronic Engineering

Universidad Central

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

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.

Original language	English
Title of host publication	2022 12th Workshop on Hyperspectral Imaging and Signal Processing
Subtitle of host publication	Evolution in Remote Sensing, WHISPERS 2022
Publisher	IEEE Computer Society
ISBN (Electronic)	9781665470698
DOIs	https://doi.org/10.1109/WHISPERS56178.2022.9955068
State	Published - 2022
Event	12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2022 - Rome, Italy Duration: 13 Sep 2022 → 16 Sep 2022

Publication series

Name	Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
Volume	2022-September
ISSN (Print)	2158-6276

Conference

Conference	12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2022
Country/Territory	Italy
City	Rome
Period	13/09/22 → 16/09/22

Keywords

Agricultural Systems
Machine Learning
Remote Sensing
Soil Quality Indicators
UAV

Access to Document

10.1109/WHISPERS56178.2022.9955068

Cite this

Diaz-Gonzalez, F. A., Correa-Florez, C. A., Vuelvas, J., Vallejo, V. E., & Patino, D. (2022). A methodology for estimating soil quality indicators in agricultural systems using UAV and machine learning. In 2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2022 (Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing; Vol. 2022-September). IEEE Computer Society. https://doi.org/10.1109/WHISPERS56178.2022.9955068

Diaz-Gonzalez, Freddy A. ; Correa-Florez, Carlos A. ; Vuelvas, José et al. / A methodology for estimating soil quality indicators in agricultural systems using UAV and machine learning. 2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2022. IEEE Computer Society, 2022. (Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing).

@inproceedings{a1c3f9b9690245ad91bf7314088e35e0,

title = "A methodology for estimating soil quality indicators in agricultural systems using UAV and machine learning",

abstract = "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.",

keywords = "Agricultural Systems, Machine Learning, Remote Sensing, Soil Quality Indicators, UAV",

author = "Diaz-Gonzalez, {Freddy A.} and Correa-Florez, {Carlos A.} and Jos{\'e} Vuelvas and Vallejo, {Victoria E.} and D. Patino",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2022 ; Conference date: 13-09-2022 Through 16-09-2022",

year = "2022",

doi = "10.1109/WHISPERS56178.2022.9955068",

language = "English",

series = "Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing",

publisher = "IEEE Computer Society",

booktitle = "2022 12th Workshop on Hyperspectral Imaging and Signal Processing",

address = "United States",

}

Diaz-Gonzalez, FA, Correa-Florez, CA, Vuelvas, J, Vallejo, VE & Patino, D 2022, A methodology for estimating soil quality indicators in agricultural systems using UAV and machine learning. in 2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2022. Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing, vol. 2022-September, IEEE Computer Society, 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2022, Rome, Italy, 13/09/22. https://doi.org/10.1109/WHISPERS56178.2022.9955068

A methodology for estimating soil quality indicators in agricultural systems using UAV and machine learning. / Diaz-Gonzalez, Freddy A.; Correa-Florez, Carlos A.; Vuelvas, José et al.
2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2022. IEEE Computer Society, 2022. (Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing; Vol. 2022-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A methodology for estimating soil quality indicators in agricultural systems using UAV and machine learning

AU - Diaz-Gonzalez, Freddy A.

AU - Correa-Florez, Carlos A.

AU - Vuelvas, José

AU - Vallejo, Victoria E.

AU - Patino, D.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Agricultural Systems

KW - Machine Learning

KW - Remote Sensing

KW - Soil Quality Indicators

KW - UAV

UR - http://www.scopus.com/inward/record.url?scp=85143134524&partnerID=8YFLogxK

U2 - 10.1109/WHISPERS56178.2022.9955068

DO - 10.1109/WHISPERS56178.2022.9955068

M3 - Conference contribution

AN - SCOPUS:85143134524

T3 - Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing

BT - 2022 12th Workshop on Hyperspectral Imaging and Signal Processing

PB - IEEE Computer Society

T2 - 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2022

Y2 - 13 September 2022 through 16 September 2022

ER -

Diaz-Gonzalez FA, Correa-Florez CA, Vuelvas J, Vallejo VE, Patino D. A methodology for estimating soil quality indicators in agricultural systems using UAV and machine learning. In 2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2022. IEEE Computer Society. 2022. (Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing). doi: 10.1109/WHISPERS56178.2022.9955068

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

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this