Deep Learning-Based Transfer Learning for Classification of Cassava Disease

  • Ademir G. Costa Junior UEA
  • Fábio S. da Silva UEA
  • Ricardo Rios UEA

Resumo


Esse artigo apresenta um comparativo de desempenho entre quatro arquiteturas de Redes Neurais Convolucionais (EfficientNet-B3, InceptionV3, ResNet50 e VGG16) na classificação de imagens de patologias da mandioca. As imagens foram obtidas de um conjunto de dados desbalanceado de uma competição. Foram utilizadas métricas adequadas para lidar com o desbalanceamento entre classes do conjunto. Os resultados indicam que a EfficientNet-B3 alcançou nessa tarefa acurácia de 87,7%, precisao de 87,8%, revocação de 87,8% e F1-Score de 87,7%. Isso sugere que o EfficientNet-B3 pode ser uma ferramenta valiosa de apoio para Agricultura Digital.

Palavras-chave: aprendizado profundo, aprendizagem por transferência, doença da mandioca

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Publicado
17/11/2024
COSTA JUNIOR, Ademir G.; DA SILVA, Fábio S.; RIOS, Ricardo. Deep Learning-Based Transfer Learning for Classification of Cassava Disease. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 21. , 2024, Belém/PA. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2024 . p. 364-375. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2024.244378.