Classificação do tipo de tomate por meio de imagem: um estudo de caso utilizando redes neurais convolucionais

• Ano de defesa: 2024
• Autor(a) principal: Oliveira, Leandro de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Medianeira; Brasil; Programa de Pós-Graduação em Tecnologias Computacionais para o Agronegócio; UTFPR
• Palavras-chave em Português: Inteligência artificial; Python (Linguagem de programação de computador); Redes neurais (Computação); Artificial intelligence; Python (Computer program language); Neural networks (Computer science); CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO; Engenharia/Tecnologia/Gestão
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/33783

The aim of this work is to use artificial neural networks to classify different types of tomatoes (variety or cultivar) on sale in establishments in the western region of Paraná. The classification process was carried out using models based on convolutional neural networks, and included image acquisition and processing, training, validation and testing of the classification models. A smartphone camera was used to build the tomato image database. The models chosen for classification were: ResNet50V2, MobileNet, MobileNetV2, Xception, EfficientNetV2B0, EfficientNetV2B1, EfficientNetV2B2 and EfficientNetV2B3, which were implemented in the Python language using the Keras API of the TensorFlow framework. The results obtained from the eight models were compared using accuracy and loss. For a test set of 12 images, significantly different from the training set, the MobileNet model performed best, with a test accuracy of 83.33% and an error of 0.622.