Version 1
: Received: 19 July 2024 / Approved: 19 July 2024 / Online: 19 July 2024 (13:38:37 CEST)
How to cite:
Gatou, P.; Tsiara, X.; Spitalas, A.; Sioutas, S.; Vonitsanos, G. Artificial Intelligence Techniques in Grapevine Research: A Comparative Study with Extensive Review on Datasets, Diseases, and Techniques Evaluation. Preprints2024, 2024071623. https://doi.org/10.20944/preprints202407.1623.v1
Gatou, P.; Tsiara, X.; Spitalas, A.; Sioutas, S.; Vonitsanos, G. Artificial Intelligence Techniques in Grapevine Research: A Comparative Study with Extensive Review on Datasets, Diseases, and Techniques Evaluation. Preprints 2024, 2024071623. https://doi.org/10.20944/preprints202407.1623.v1
Gatou, P.; Tsiara, X.; Spitalas, A.; Sioutas, S.; Vonitsanos, G. Artificial Intelligence Techniques in Grapevine Research: A Comparative Study with Extensive Review on Datasets, Diseases, and Techniques Evaluation. Preprints2024, 2024071623. https://doi.org/10.20944/preprints202407.1623.v1
APA Style
Gatou, P., Tsiara, X., Spitalas, A., Sioutas, S., & Vonitsanos, G. (2024). Artificial Intelligence Techniques in Grapevine Research: A Comparative Study with Extensive Review on Datasets, Diseases, and Techniques Evaluation. Preprints. https://doi.org/10.20944/preprints202407.1623.v1
Chicago/Turabian Style
Gatou, P., Spyros Sioutas and Gerasimos Vonitsanos. 2024 "Artificial Intelligence Techniques in Grapevine Research: A Comparative Study with Extensive Review on Datasets, Diseases, and Techniques Evaluation" Preprints. https://doi.org/10.20944/preprints202407.1623.v1
Abstract
In the last few years, the agricultural field has undergone a digital transformation, incorporating artificial intelligence systems to make good employment of the growing volume of data from various sources and derive value from it. Within artificial intelligence, machine learning is a powerful tool for confronting the numerous challenges of developing knowledge-based farming systems. This study aims to comprehensively review the current scientific literature from 2017 to 2023, emphasizing machine learning in agriculture, especially viticulture, to detect and predict grape infections. Most of these studies (88%) were conducted within the last five years. A variety of machine learning algorithms were used, with those belonging to the neural network (CNN) standing out having the best results most of the time. Out of the list of diseases, the ones most researched were Grapevine Yellow, Flavescence Dorée, Esca, Downy mildew, Leafroll, Pierce’s, and Root Rot. Also, some other fields were studied which are water management, plant deficiencies, and classification. Because of the difficulty of the topic, it was collected all datasets that were available about grapevines, and it is described each dataset with the type of data (e.g., statistical, images, type of images), along with the number of images where it was mentioned. This work provides a unique source of information for a general audience comprising AI researchers, agricultural scientists, wine grape growers, and policymakers. Among others, its outcomes could be effective at curbing diseases in viticulture, which in turn will drive sustainable gains and boost success. Additionally, it could help build resilience in related farming industries such as winemaking.
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.
