Version 1
: Received: 7 March 2024 / Approved: 8 March 2024 / Online: 8 March 2024 (15:31:19 CET)
Version 2
: Received: 2 May 2024 / Approved: 4 May 2024 / Online: 6 May 2024 (08:47:17 CEST)
How to cite:
Carneiro, G.A.; Cunha, A.; Sousa, J. Deep Learning for Automatic Grapevine Varieties Identification: A Brief Review. Preprints2024, 2024030484. https://doi.org/10.20944/preprints202403.0484.v1
Carneiro, G.A.; Cunha, A.; Sousa, J. Deep Learning for Automatic Grapevine Varieties Identification: A Brief Review. Preprints 2024, 2024030484. https://doi.org/10.20944/preprints202403.0484.v1
Carneiro, G.A.; Cunha, A.; Sousa, J. Deep Learning for Automatic Grapevine Varieties Identification: A Brief Review. Preprints2024, 2024030484. https://doi.org/10.20944/preprints202403.0484.v1
APA Style
Carneiro, G.A., Cunha, A., & Sousa, J. (2024). Deep Learning for Automatic Grapevine Varieties Identification: A Brief Review. Preprints. https://doi.org/10.20944/preprints202403.0484.v1
Chicago/Turabian Style
Carneiro, G.A., António Cunha and Joaquim Sousa. 2024 "Deep Learning for Automatic Grapevine Varieties Identification: A Brief Review" Preprints. https://doi.org/10.20944/preprints202403.0484.v1
Abstract
The Eurasian grapevine (\textit{Vitis vinifera L.}) is the most widely grown horticultural crop in the world and is important for the economy of many countries. In the wine production chain, grape varieties play an important role, as they directly influence the authenticity and classification of the product. Identifying the different grape varieties is therefore fundamental for quality control and control activities, as well as for regulating production. Currently, ampelography and molecular analysis are the main approaches to identifying grape varieties. However, both methods have limitations. Ampelography is subjective and prone to errors and is experiencing enormous difficulties as ampelographers are increasingly scarce. On the other hand, molecular analyses are very demanding in terms of cost and time. In this scenario, Deep Learning (DL) methods have emerged as a classification alternative to deal with the scarcity of ampelographers and avoid molecular analyses. In this study, the most recent and current methods for identifying grapevine varieties using DL classification-based approaches are presented through a systematic literature review. The steps of the standard DL-based classification pipeline were described for the 18 most relevant studies found in the literature, highlighting their pros and cons. Potential directions for improving this field of research were also presented.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
