135781 20260217205549.0 doi 10.1080/17538947.2024.2359565 sideral 138803 ART-2024-138803 eng Suaza-Medina, Mario E. Universidad de Zaragoza (orcid)0000-0002-2112-3478 Evaluating the efficiency of NDVI and climatic data in maize harvest prediction using machine learning 2024 Access copy available to the general public Unrestricted 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. info:eu-repo/semantics/openAccess by-nc https://creativecommons.org/licenses/by-nc/4.0/deed.es 4.9 2024 GEOGRAPHY, PHYSICAL 10 / 67 = 0.149 2024 Q1 T1 REMOTE SENSING 14 / 65 = 0.215 2024 Q1 T1 1.111 2024 Computer Science Applications 2024 Q1 Software 2024 Q1 Earth and Planetary Sciences (miscellaneous) 2024 Q1 6.1 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Laguna, Jorge Universidad de Zaragoza Béjar, Rubén Universidad de Zaragoza (orcid)0000-0001-7866-3793 Zarazaga-Soria, F. Javier Universidad de Zaragoza (orcid)0000-0002-6557-2494 Lacasta, Javier Universidad de Zaragoza (orcid)0000-0003-3071-5819 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 17, 1 (2024), [16 pp.] Int. j. Digital Earth International Journal of Digital Earth 1753-8947 1733179 http://zaguan.unizar.es/record/135781/files/texto_completo.pdf Versión publicada 1085662 http://zaguan.unizar.es/record/135781/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:135781 articulos driver 2026-02-17-20:39:39 ARTICLE