Resumen

PORTILLO MENDOZA, Pedro Miguel  y  PONCE ALVINO, Jefferson Peter. Optimum classification of coffee fruits according to their maturity by means of a control algorithm. Rev. Inv. Cs. Agro. y Vet. [online]. 2022, vol.6, n.18, pp.441-452.  Epub 04-Nov-2022. ISSN 2664-0902.  https://doi.org/10.33996/revistaalfa.v6i18.181.

The purpose of this research is to know to what extent an algorithm-controlled automatic system allows the optimal classification of coffee fruits according to the degree of maturity, identifying them by their color. For which a multilayer neural network was developed using MATLAB which was implemented in a STM32F103C8 microcontroller, using as input data the RGB color mode characteristics of 300 samples of coffee fruits in different stages of maturation, delivered by a sensor of color TCS3200, which allowed having a database of different maturity levels used to train the multilayer type neural network with 3 inputs; 3 hidden layers with 6 neurons in the first layer and 3 in the other two, as well as one neuron in the output layer. The data was organized according to the state of maturity of the fruits, in "Optimal Maturity" or "Non-Optimal Maturity". The system was tested with 60 coffee fruits, obtaining as a result an efficiency of 96.67% and an error rate of 3.33%; thus confirming that the classification system through the control of the algorithm and multilayer neural network designed, identifies and classifies based on the maturity of the coffee fruits optimally.

Palabras clave : Coffee classification; Algorithm; RGB colors; Neural network; Control algorithm.

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