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Title: Diseases detection in citrus fruits using convolutional neural networks
Author: López Laso, Fernando
Director: Ventura Royo, Carles  
Tutor: Isern Alarcón, David
Others: Universitat Oberta de Catalunya
Keywords: citrus diseases
mobile applications
convolutional neural network
Issue Date: Jun-2018
Publisher: Universitat Oberta de Catalunya
Abstract: The Deep Learning is becoming the most promising technique for computer vision applications. In this work, deep learning techniques are used and a convoluted neuronal network is trained to help to predict fruit diseases. As this field is extremely wide, the work is focused in the case of one of the families that combines both being a very important economic market, and having many problems of diseases that cause loss of fruit value among others problems. Then, with the network trained and validated, an application is developed for Android mobile devices to be able to use it as a tool to help decision-making of professionals in the phytosanitary sector. To develop the network, the PyTorch library has been used. This library is programmed with the Python programming language, which is almost the standard de facto in the industry. In addition, the Android application is developed with Kotlin, and to be able to use the neuronal network developed in the first part of the thesis in the Android operative system, TensorFlow Lite library has been also used.
Language: English
Appears in Collections:Bachelor thesis, research projects, etc.

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flopez215VideoPresentacio0618.mp449.13 MBMP4View/Open
flopez215TFM0618memoria.pdfMemoria del TFG14.44 MBAdobe PDFView/Open
flopez215TFG0618presentación.pdfPresentación del TFG3.98 MBAdobe PDFView/Open

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