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http://hdl.handle.net/10609/98767
Title: | Modelo de aprendizaje profundo/red neuronal convolucional (CNN) para clasificación de calidad de ácidos grasos por imágenes de semillas de Helianthus annuus |
Author: | Vega Arias, Juan Manuel |
Tutor: | Vegas Lozano, Esteban Reverter, Ferran |
Others: | MORENO PEREZ, ANTONIO JAVIER |
Abstract: | The fatty acids content classification in seeds for their later use in industry is a long and complicated process which aims to select the different seeds that would be used with the purposes each of these seeds are of best use. These purposes are, at the same time, determined by the quality content of the fatty acids in the seed. Deep neural networks, especially convolutional neural networks, have shown a remarkable capacity for image classification and pattern abstraction in many different fields, obtaining better accuracy, and faster prediction results than those obtained by classic or human methods. In this work, we build a convolutional neural network model which can classify the sunflower seeds fatty acids quality through their images. The model was developed separating the work in two main sections. First, an experimental portion in which we collected the necessary data to build our own data set from scratch to train the neural network, and second, the analytic component in which we developed the model using the data we previously collected. This model shows a high accuracy classifying different types of sunflower (Helianthus annuus L.) seeds used for different purposes depending on their fatty acids quality content. |
Keywords: | convolutional neural network image classification deep learning |
Document type: | info:eu-repo/semantics/masterThesis |
Issue Date: | 26-Jun-2019 |
Publication license: | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
Appears in Collections: | Trabajos finales de carrera, trabajos de investigación, etc. |
Files in This Item:
File | Description | Size | Format | |
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jvegaarTFM0619memoria.pdf | Memoria de TFM | 3,31 MB | Adobe PDF | View/Open |
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