As mentioned by the Food and Agriculture Organization of the United Nations, agriculture has a primary role in food security. Given the advantageous conditions that Colombia has as a biodiverse country, creating and implementing sustainable and comprehensive agricultural systems is essential to generate agricultural decision-making tools. Therefore, this paper displays the design and deployment (training–validation) of a neuro-fuzzy model for the relevant agricultural production in Colombia. Four different configurations are proposed according to the data collected and the variables identified. The results show that a remarkable prediction of the models (configurations) is achieved by using training and validation data.

This article develops the design, training, and validation of a computational model to predict the exportation of traditional Colombian products using artificial neural networks. This work aims to obtain a model using a single multilayer neural network. The number of historical input data (delays), the number of layers, and the number of neurons were considered for the neural network design. In this way, an experimental design of 64 configurations of the neural network was performed. The main arduousness addressed in this work is the significant difference (in tons) in the values of the considered products. The results show the effect that occurs due to the different range values, and one of the proposals made allows this limitation to be handled appropriately. In summary, this work seeks to provide essential information for formulating a model for efficient and practical application.