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 and Machine Learning for Automatic Grapevine Varieties Identification: A Brief Review. Preprints2024, 2024030484. https://doi.org/10.20944/preprints202403.0484.v2
Carneiro, G. A.; Cunha, A.; Sousa, J. Deep Learning and Machine Learning for Automatic Grapevine Varieties Identification: A Brief Review. Preprints 2024, 2024030484. https://doi.org/10.20944/preprints202403.0484.v2
Carneiro, G. A.; Cunha, A.; Sousa, J. Deep Learning and Machine Learning for Automatic Grapevine Varieties Identification: A Brief Review. Preprints2024, 2024030484. https://doi.org/10.20944/preprints202403.0484.v2
APA Style
Carneiro, G. A., Cunha, A., & Sousa, J. (2024). Deep Learning and Machine Learning for Automatic Grapevine Varieties Identification: A Brief Review. Preprints. https://doi.org/10.20944/preprints202403.0484.v2
Chicago/Turabian Style
Carneiro, G. A., António Cunha and Joaquim Sousa. 2024 "Deep Learning and Machine Learning for Automatic Grapevine Varieties Identification: A Brief Review" Preprints. https://doi.org/10.20944/preprints202403.0484.v2
Abstract
The Eurasian grapevine (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 inspection 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) and Machine Learning (ML) methods have emerged as a classification alternative to deal with the scarcity of ampelographs 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 classification pipeline of the 31 studies found in the literature was described, highlighting its pros and cons. Most of the studies used DL-based models trained with leaf images acquired in a controlled environment at a maximum distance of 1.2 metres to classify grape varieties. In addition, there is a large gap between practical applications and the datasets used: a great lack of varieties, limited data acquired in the field and a lack of tests on plants under adverse conditions. Potential directions for improving this area 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.
