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August  2021, 1(3): 186-198. doi: 10.3934/steme.2021014

## Neural network training in SCILAB for classifying mango (Mangifera indica) according to maturity level using the RGB color model

 1 Department of Mechatronics, Instituto Politecnico Nacional, CICATA Unidad Queréro, CP 76090, México

* Correspondence: ecastilloca@ipn.mx; Tel: +52-442-229-0804

Received  June 2021 Revised  July 2021 Published  August 2021

Industries that use fruits as raw materials must, at some point in the process, classify them to discard the unsuitable ones and thus ensure the quality of the final product. To produce mango nectar, it is necessary to ensure that the mango is mature enough to start the extraction of the nectar; however, sorting thousands of mangoes may require many people, who can easily lose attention and reduce the accuracy of the result. Such kind of decision can be supported by current Artificial Intelligence techniques. The theoretical details of the processing are presented, as well as the programming code of the neural network using SCILAB as a computer language; the code includes the color extraction from mango images. SCILAB programming is simple, efficient and does not require computers with large processing capacity. The classification was validated with 30 images (TIF format) of Manila variety mango; the mangoes were placed on a blue background to easily separate the background from the object of interest. Four and six mangoes were used to train the neural network. This application of neural networks is part of an undergraduate course on artificial intelligence, which shows the potential of these techniques for solving real and concrete problems.

Citation: Eduardo Castillo-Castaneda. Neural network training in SCILAB for classifying mango (Mangifera indica) according to maturity level using the RGB color model. STEM Education, 2021, 1 (3) : 186-198. doi: 10.3934/steme.2021014
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##### References:
].">Figure 1.  Manual mango classification [2].
Stages for the implementation of an AI computer program.
Levels of mango maturity.
Segmentation applied to separate the airplane from the sky (including clouds).
Some colors and the corresponding RGB values.
a), b), c), d) Color extraction images, e) SCILAB code.
Model of an artificial neuron, named perceptron, with 3 input variables.
An ANN with 3 layers.
SCILAB code to calculate de ANN output.
Training of an ANN from known values of $X$ and $Y$ vectors.
SCILAB program for ANN training.
Mangoes selected for ANN training.
Input and output variables for ANN training.
Preliminary result of the classification of 30 mangoes.
Green, medium, and mature mangoes for ANN training.
Result with improved training using 6 mangoes.
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