Publicação
Leaf-based species recognition using convolutional neural networks
| Resumo: | Identifying plant species is an important activity in specie control and preservation. The identification process is carried out mainly by botanists, consisting of a comparison of already known specimens or using the aid of books, manuals or identification keys. Artificial Neural Networks have been shown to perform well in classification problems and are a suitable approach for species identification. This work uses Convolutional Neural Networks to classify tree species by leaf images. In total, 29 species were collected. This work analyzed two network models, Darknet-19 and GoogLeNet (Inception-v3), presenting a comparison between them. The Darknet and GoogLeNet models achieved recognition rates of 86.2% and 90.3%, respectively. |
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| Autores principais: | Pires, Willian Oliveira |
| Outros Autores: | Fernandes, Ricardo Corso; Paula Filho, Pedro Luiz de; Candido Junior, Arnaldo; Teixeira, João Paulo |
| Assunto: | Leaf recognition Tree classification |
| Ano: | 2021 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| Resumo: | Identifying plant species is an important activity in specie control and preservation. The identification process is carried out mainly by botanists, consisting of a comparison of already known specimens or using the aid of books, manuals or identification keys. Artificial Neural Networks have been shown to perform well in classification problems and are a suitable approach for species identification. This work uses Convolutional Neural Networks to classify tree species by leaf images. In total, 29 species were collected. This work analyzed two network models, Darknet-19 and GoogLeNet (Inception-v3), presenting a comparison between them. The Darknet and GoogLeNet models achieved recognition rates of 86.2% and 90.3%, respectively. |
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