Toward Grapevine Digital Ampelometry Through Vision Deep Learning Models

• Type: Article in International Scientific Journal
• Date: 2023

Authors

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

Journal

Title: IEEE Sensors Journal

Vol. 23

Pages: 1-1

ISSN: 1530-437X

Publisher: IEEE

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 3 Citations

Other information

Authenticus ID: 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%.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8