Citas bibligráficas
Lazo, W., (2019). Espectroscopia con infrarrojo y técnicas de Machine learning y Deep learning para la detección y clasificación de frutas para la agroindustria. Caso: arándanos - Empresa Talsa - 2018 [Tesis, Universidad Privada Antenor Orrego - UPAO]. https://hdl.handle.net/20.500.12759/5106
Lazo, W., Espectroscopia con infrarrojo y técnicas de Machine learning y Deep learning para la detección y clasificación de frutas para la agroindustria. Caso: arándanos - Empresa Talsa - 2018 [Tesis]. : Universidad Privada Antenor Orrego - UPAO; 2019. https://hdl.handle.net/20.500.12759/5106
@mastersthesis{renati/373553,
title = "Espectroscopia con infrarrojo y técnicas de Machine learning y Deep learning para la detección y clasificación de frutas para la agroindustria. Caso: arándanos - Empresa Talsa - 2018",
author = "Lazo Aguirre, Walter Aurelio",
publisher = "Universidad Privada Antenor Orrego - UPAO",
year = "2019"
}
INFRARED SPECTROSCOPY AND MACHINE LEARNING TECHNIQUES AND DEEP LEARNING FOR THE DETECTION AND CLASSIFICATION OF FRUITS FOR AGRIBUSINESS. CASE: ARANDANS - COMPANY TalSA -2018 The companies marketing fruit, have the need to optimize the selection and classification of the fruits they market, specifically those products that will be sent abroad where high quality indexes are required. They need to ensure that the fruit selection and sorting process is carried out with precision to obtain a high quality product that meets the demands of the customers. There are teams that can perform this classification work, but they are very expensive for their acquisition as well as their maintenance. For the above, this thesis develops a proposal based on the application of near infrared spectroscopy, Machine Learning and Deep Learning for the detection and classification of fruits for the agroindustry. Specifically, the classification of blueberries in TalSA is taken as a case study in order to establish an alternative tool to make the classification of blueberries, which allows to reduce the cost, time and optimize the process of detection and classification of Fruits. To achieve the above, the state of the art of machine learning, deep learning, and deep neural networks was reviewed, the use of spectroscopy in the recognition of fruits and their properties was reviewed, the process of selection and classification of blueberries in the TalSA Company was analized. Then, using a near infrared spectroscopy equipment, NIR, the spectrum of the blueberry samples was obtained. With these spectra, digitized, the training of a neural network was carried out using the Python programming language and the Keras platform with TensorFlow. Then, after training, it was used the neural network to carry out the testing stage, with the NIR spectra of new blueberry samples, finding that blueberries can be classified with an accuracy of 92%, which confirms the working hypothesis . By Br. Walter Aurelio Lazo Aguirre
IMPORTANTE
La información contenida en este registro es de entera responsabilidad de la universidad, institución o escuela de educación superior que administra el repositorio académico digital donde se encuentra el trabajo de investigación y/o proyecto, los cuales son conducentes a optar títulos profesionales y grados académicos. SUNEDU no se hace responsable por los contenidos accesibles a través del Registro Nacional de Trabajos de Investigación – RENATI.