Predictive model for cocoa yield in Santander using Supervised Machine Learning

Andrea A. Gamboa, Paula A. Cáceres, Henry Lamos, Diego A. Zárate, David E. Puentes

Producción científica: Libro / Capitulo del libro / InformeLibros de Investigaciónrevisión exhaustiva

4 Citas (Scopus)

Resumen

Supervised Machine Learning represent a good alternative for the agriculture, in the way that it allows to support farmers, government and other stakeholders in the decision-making process based on crop yield forecast, which are defined as the volume of product harvested per unit area. This investigation has as object of study an experimental culture of cocoa in Santander, located in the research center La Suiza, and its purpose is to predict the yield of the crop through a set of photosynthetic, morphological, climatic, chemical and physical variables. Using the Generalized Linear Model (GLM) and the Vector Support Machines (SVM), the explanatory variables with the greatest impact were identified both negatively and positively on the cocoa crop yield variable. The construction and comparison of the results of the two models, was useful to ratify that the explanatory variables: Diameter of the trunk, Phosphorus (P), Magnesium (Mg), % Sand, % Hum/Grav, Radiation, Temperature, Humidity and Rains accumulated are the variables that explain to a greater extent the performance of the cocoa crop.

Idioma originalInglés
Título de la publicación alojada2019 22nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2019 - Conference Proceedings
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728114910
DOI
EstadoPublicada - abr. 2019
Publicado de forma externa
Evento22nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2019 - Bucaramanga, Colombia
Duración: 24 abr. 201926 abr. 2019

Serie de la publicación

Nombre2019 22nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2019 - Conference Proceedings

Conferencia

Conferencia22nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2019
País/TerritorioColombia
CiudadBucaramanga
Período24/04/1926/04/19

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