AgroML: An Open-Source Repository to Forecast Reference Evapotranspiration in Different Geo-Climatic Conditions Using Machine Learning and Transformer-Based Models

AgroML: An Open-Source Repository to Forecast Reference Evapotranspiration in Different Geo-Climatic Conditions Using Machine Learning and Transformer-Based Models

Accurately forecasting reference evapotranspiration (ET<sub>0</sub>) values is crucial to improve crop irrigation scheduling, allowing anticipated planning decisions and optimized water resource management and agricultural production. In this work, a recent state-of-the-art architecture...

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Bibliographic Details
Main Authors: Juan Antonio Bellido-Jiménez, Javier Estévez, Joaquin Vanschoren, Amanda Penélope García-Marín
Format: Article
Language:English
Published: MDPI AG 2022-03-01
Series:Agronomy
Subjects:
machine learning
transformers
neural networks
support vector machine
reference evapotranspiration
forecasting
Online Access:https://www.mdpi.com/2073-4395/12/3/656

Internet

https://www.mdpi.com/2073-4395/12/3/656

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