TY - GEN
T1 - Estimating Soil Quality Indicators Using Remote Sensing Data
T2 - 6th IEEE Colombian Conference on Automatic Control, CCAC 2023
AU - Diaz-Gonzalez, Freddy A.
AU - Vallejo, Victoria E.
AU - Vuelvas, Jose
AU - Patino, Diego
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Intensive agriculture has resulted in a decline in soil quality, presenting significant challenges in terms of increasing agricultural productivity while maintaining environmental sustainability. Consequently, farmers require methodologies that integrate low-cost technologies for data collection and processing with traditional soil quality assessment tools to estimate soil quality indicators (SQIs). This study presents an application of machine learning regression models to estimate soil quality in local-scale agricultural systems through the processing of a georeferenced-multidimensional database. Additionally, the impact of eight standardization methods on the performance of the machine learning models is investigated. The regression analysis results for the analyzed SQIs demonstrate satisfactory performance based on two metrics: negative mean square error and r2. These findings contribute to the establishment of an estimation model for SQIs using machine learning algorithms. Notably, indicators directly linked to chemical fertilizers, such as nitrogen, potassium, and phosphorus, exhibit performance levels of 70%, 71%, and 92%, respectively.
AB - Intensive agriculture has resulted in a decline in soil quality, presenting significant challenges in terms of increasing agricultural productivity while maintaining environmental sustainability. Consequently, farmers require methodologies that integrate low-cost technologies for data collection and processing with traditional soil quality assessment tools to estimate soil quality indicators (SQIs). This study presents an application of machine learning regression models to estimate soil quality in local-scale agricultural systems through the processing of a georeferenced-multidimensional database. Additionally, the impact of eight standardization methods on the performance of the machine learning models is investigated. The regression analysis results for the analyzed SQIs demonstrate satisfactory performance based on two metrics: negative mean square error and r2. These findings contribute to the establishment of an estimation model for SQIs using machine learning algorithms. Notably, indicators directly linked to chemical fertilizers, such as nitrogen, potassium, and phosphorus, exhibit performance levels of 70%, 71%, and 92%, respectively.
KW - Agricultural Systems
KW - Machine Learning
KW - Remote Sensing
KW - Soil Quality Indicators
KW - UAV
UR - http://www.scopus.com/inward/record.url?scp=85180806810&partnerID=8YFLogxK
U2 - 10.1109/CCAC58200.2023.10333526
DO - 10.1109/CCAC58200.2023.10333526
M3 - Conference contribution
AN - SCOPUS:85180806810
T3 - Proceedings of the 2023 IEEE 6th Colombian Conference on Automatic Control, CCAC 2023
BT - Proceedings of the 2023 IEEE 6th Colombian Conference on Automatic Control, CCAC 2023
A2 - Martiinez, Diana Marcela Ovalle
A2 - Alfonso, Luis Francisco Combita
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 17 October 2023 through 20 October 2023
ER -