000135781 001__ 135781
000135781 005__ 20240614091948.0
000135781 0247_ $$2doi$$a10.1080/17538947.2024.2359565
000135781 0248_ $$2sideral$$a138803
000135781 037__ $$aART-2024-138803
000135781 041__ $$aeng
000135781 100__ $$aSuaza-Medina, Mario E.$$uUniversidad de Zaragoza
000135781 245__ $$aEvaluating the efficiency of NDVI and climatic data in maize harvest prediction using machine learning
000135781 260__ $$c2024
000135781 5060_ $$aAccess copy available to the general public$$fUnrestricted
000135781 5203_ $$aAccurate anticipation of the maize harvest date is important in the agricultural market, as it ensures the sustainability of food production in response to the increasing global demand for food. This paper proposes a predictive model to determine the optimal harvest time in maize plots using the Normalised Difference Vegetation Index (NDVI) and climatological data. These variables were oversampled and used to train various models, including Random Forest (RF), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting Machine (XGBoost), CatBoost and Support Vector Machine (SVM). Bayesian optimisation has been used to find the best hyperparameters and Shapley values to identify the variables that exert the most significant influence on the prediction in each model instance. As a result of this approach, a model with an accuracy of 92.1% and an Area Under the Curve (AUC) of 0.935 was developed. The variables that determined these results were atmospheric pressure, mean temperature, precipitation, NDVI, and precipitation.
000135781 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc$$uhttp://creativecommons.org/licenses/by-nc/3.0/es/
000135781 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000135781 700__ $$aLaguna, Jorge$$uUniversidad de Zaragoza
000135781 700__ $$0(orcid)0000-0001-7866-3793$$aBéjar, Rubén$$uUniversidad de Zaragoza
000135781 700__ $$0(orcid)0000-0002-6557-2494$$aZarazaga-Soria, F. Javier$$uUniversidad de Zaragoza
000135781 700__ $$0(orcid)0000-0003-3071-5819$$aLacasta, Javier$$uUniversidad de Zaragoza
000135781 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000135781 773__ $$g17, 1 (2024), [16 pp.]$$pInt. j. Digital Earth$$tInternational Journal of Digital Earth$$x1753-8947
000135781 8564_ $$s1733179$$uhttps://zaguan.unizar.es/record/135781/files/texto_completo.pdf$$yVersión publicada
000135781 8564_ $$s1085662$$uhttps://zaguan.unizar.es/record/135781/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000135781 909CO $$ooai:zaguan.unizar.es:135781$$particulos$$pdriver
000135781 951__ $$a2024-06-14-09:00:17
000135781 980__ $$aARTICLE