Toward Grapevine Digital Ampelometry Through Vision Deep Learning Models

• Tipo: Artigo em Revista Científica Internacional
• Data: 2023

Autores

Magalhaes, SC (Autor) Outra; Castro, L (Autor) Outra; Rodrigues, L (Autor) Outra; Padilha, TC (Autor) Outra; de Carvalho, F (Autor) Outra; Filipe Neves Santos (Autor) FEUP; Pinho, T (Autor) Outra; Moreira, G (Autor) Outra; Cunha, J (Autor) Outra; Mario Cunha (Autor) FCUP; Silva, P (Autor) Outra; António Paulo Moreira (Autor) FEUP

Revista

Título: IEEE Sensors Journal

Vol. 23

Páginas: 1-1

ISSN: 1530-437X

Editora: IEEE

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 2 Citações

Outras Informações

ID Authenticus: P-00Y-5FP

DOI: 10.1109/jsen.2023.3261544

Abstract (EN): Several thousand grapevine varieties exist, with even more naming identifiers. Adequate specialized labor is not available for proper classification or identification of grapevines, making the value of commercial vines uncertain. Traditional methods, such as genetic analysis or ampelometry, are time-consuming, expensive, and often require expert skills that are even rarer. New vision-based systems benefit from advanced and innovative technology and can be used by nonexperts in ampelometry. To this end, deep learning (DL) and machine learning (ML) approaches have been successfully applied for classification purposes. This work extends the state of the art by applying digital ampelometry techniques to larger grapevine varieties. We benchmarked MobileNet v2, ResNet-34, and VGG-11-BN DL classifiers to assess their ability for digital ampelography. In our experiment, all the models could identify the vines' varieties through the leaf with a weighted F1 score higher than 92%.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8