Russian Journal of Biological Physics and Chemisrty Russian Journal of Biological Physics and Chemisrty АКТУАЛЬНЫЕ ВОПРОСЫ БИОЛОГИЧЕСКОЙ ФИЗИКИ И ХИМИИ 2499-9962 54574 Моделирование в биофизике Modelling in biophycis Моделирование в биофизике Multilayer perceptron neural network for the prediction of the ripening stages of fruits and vegetables Multilayer perceptron neural network for the prediction of the ripening stages of fruits and vegetables Hernandez-Aguirre J A Hernandez-Aguirre J A ximikad09@mail.ru Arzola-Flores J A Arzola-Flores J A Vidal-Robles E Vidal-Robles E Saviñon-Flores M F Saviñon-Flores M F Maldonado-García A Maldonado-García A Guarneros-Bejarano G Guarneros-Bejarano G Rojas-Rodríguez J F Rojas-Rodríguez J F Hernandez-Santiago A A Hernandez-Santiago A A Meritorious Autonomous University of Puebla ru Meritorious Autonomous University of Puebla ru Meritorious Autonomous University of Puebla ru Meritorious Autonomous University of Puebla ru Meritorious Autonomous University of Puebla ru Meritorious Autonomous University of Puebla ru Meritorious Autonomous University of Puebla ru Meritorious Autonomous University of Puebla ru Meritorious Autonomous University of Puebla ru Meritorious Autonomous University of Puebla ru Meritorious Autonomous University of Puebla ru Meritorious Autonomous University of Puebla ru Meritorious Autonomous University of Puebla ru Meritorious Autonomous University of Puebla ru Meritorious Autonomous University of Puebla ru Meritorious Autonomous University of Puebla ru 25 12 2020 25 12 2020 5 4 609 613 20 12 2020 20 12 2020 https://rusjbpc.ru/en/nauka/article/54574/view

We propose the implementation of Machine Learning techniques for the classification of fruits and their ripening days using photographs of them. As a supervised learning method, a multilayer perceptron neural network was used. The results obtained from the neural network were compared with the K-nearest neighbors method through a Bayesian t-test, finding that the neural network is a better classifier of the type of fruit. Finally, Hierarchical Clustering was used to determine the ripening days of the fruits. The results obtained show that it is possible to generalize the neural network to improve its precision by increasing the variety of fruits and the size of the database.

We propose the implementation of Machine Learning techniques for the classification of fruits and their ripening days using photographs of them. As a supervised learning method, a multilayer perceptron neural network was used. The results obtained from the neural network were compared with the K-nearest neighbors method through a Bayesian t-test, finding that the neural network is a better classifier of the type of fruit. Finally, Hierarchical Clustering was used to determine the ripening days of the fruits. The results obtained show that it is possible to generalize the neural network to improve its precision by increasing the variety of fruits and the size of the database.

 neural network ripening stages prediction neural network ripening stages prediction

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