Evaluating the efficiency of NDVI and climatic data in maize harvest prediction using machine learning
Suaza-Medina, Mario E. (Universidad de Zaragoza) ; Laguna, Jorge (Universidad de Zaragoza) ; Béjar, Rubén (Universidad de Zaragoza) ; Zarazaga-Soria, F. Javier (Universidad de Zaragoza) ; Lacasta, Javier (Universidad de Zaragoza)
Resumen: Accurate anticipation of the maize harvest date is important in the agricultural market, as it ensures the sustainability of food production in response to the increasing global demand for food. This paper proposes a predictive model to determine the optimal harvest time in maize plots using the Normalised Difference Vegetation Index (NDVI) and climatological data. These variables were oversampled and used to train various models, including Random Forest (RF), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting Machine (XGBoost), CatBoost and Support Vector Machine (SVM). Bayesian optimisation has been used to find the best hyperparameters and Shapley values to identify the variables that exert the most significant influence on the prediction in each model instance. As a result of this approach, a model with an accuracy of 92.1% and an Area Under the Curve (AUC) of 0.935 was developed. The variables that determined these results were atmospheric pressure, mean temperature, precipitation, NDVI, and precipitation.
Idioma: Inglés
DOI: 10.1080/17538947.2024.2359565
Año: 2024
Publicado en: International Journal of Digital Earth 17, 1 (2024), [16 pp.]
ISSN: 1753-8947
Tipo y forma: Article (Published version)
Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)
You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes.
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Idioma: Inglés
DOI: 10.1080/17538947.2024.2359565
Año: 2024
Publicado en: International Journal of Digital Earth 17, 1 (2024), [16 pp.]
ISSN: 1753-8947
Tipo y forma: Article (Published version)
Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)
You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes. Exportado de SIDERAL (2024-06-14-09:00:17)
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Record created 2024-06-14, last modified 2024-06-14