Study of Machine Learning Techniques for the Estimation of Soil Moisture in Agriculture

Authors

DOI:

https://doi.org/10.61467/2007.1558.2024.v15i4.502

Keywords:

Soil moisture, machine learning, regression models

Abstract

Soil moisture is crucial in various fields and monitoring it to guide irrigation is challenging. Machine learning has emerged as a promising tool to predict soil moisture levels accurately. This study evaluates machine learning techniques for this task, training models with meteorological variables and direct soil moisture measurements. Four machine learning algorithms were implemented, highlighting the Gradient Boosting Regressor as the most effective. In addition, a processed data set that combines meteorological and soil moisture measurements is presented, hoping it will be helpful for future research. This approach seeks to improve the compression and predictability of soil moisture, which is crucial for agricultural planning and water management in agriculture

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Published

2024-11-04

How to Cite

Zavala Díaz, N. A., Olivares-Rojas, J. C., Zavala Díaz, J., Reyes Archundia, E., Téllez Anguiano, A. del C., Chávez Campos, G. M., & Méndez Patiño, A. (2024). Study of Machine Learning Techniques for the Estimation of Soil Moisture in Agriculture. International Journal of Combinatorial Optimization Problems and Informatics, 15(4), 61–71. https://doi.org/10.61467/2007.1558.2024.v15i4.502

Issue

Section

COMIA

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