(untitled) (Publicaciones en Ciencias y Tecnología, 2012)
Banana is an important crop in Latin America, for both large scale and small farmers. In Venezuela, the main production centers are in the area of Sur del Lago de Maracaibo. Knowing the crop yield is vital due to the need to maximize the investment-pro_t, and availability of such information in advance helps in the decision-making process of the production unit. The purpose of this research is to evaluate the ability of artificial neural networks to predict the yield of a banana crop, employing the best predictors dataset, given the physical characteristics of the soil and the chemical profile of leaf tissue. Generalized regression networks trained with the leave-one-out strategy, and two types of data transformations were used in this study. It was found that neural networks were excellent tools for predicting the yield of banana crop. The physicochemical profiles of soil and leaf tissue were suitable descriptors of the response variable. Among the data evaluated in this study, it was found that the physical data of 20-40 cm of soil was, after standardization of the training data, the best predictor group.