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Modeling of artificial neural networks applied to the maturation prediction from bananas quality parameters

Abstract

The banana tree (Musa spp.) is responsible for providing one of the most consumed and appreciated fruits in all regions of the world, being grown mainly in countries with tropical climate. In this context, several rapid management systems developed to simulate growth, yield, as well as the production of different crops based on different parameters. This work seeks to create a database from the determination of the quality of bananas in 4 ripening stages, and in this way to apply the mathematical modeling Artificial Neural Network to create a classifier capable of predicting the stage of ripening of the fruits. At the end, it will be possible to make a prediction of the point of ripening of the fruits, aiming the process, which will cause the minimization of errors due to subjectivity, allowing a fairer remuneration to the producer and standardized products available to the consumer. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
DE SOUZA, ANGELA VACARO; DE MELLO, JESSICA MARQUES; DA SILVA FAVARO, VITORIA FERREIRA; DOS SANTOS, TAYLA GABRIELLY FERREIRA; DOS SANTOS, GABRIEL PEREIRA; DE LUCCA SARTORI, DIOGO; FERRARI PUTTI, FERNANDO. Metabolism of bioactive compounds and antioxidant activity in bananas during ripening. JOURNAL OF FOOD PROCESSING AND PRESERVATION, v. 45, n. 11 SEP 2021. Web of Science Citations: 0.

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