Artificial intelligence-driven classification method of grapevine major phenological stages using conventional RGB imaging

Artificial intelligence-driven classification method of grapevine major phenological stages using conventional RGB imaging

Accurate monitoring of grapevine phenological stages is essential for optimising vineyard management. This study evaluates the performance of three deep learning architectures (ResNet-34, YOLOv11-Classification and Vision Transformer (ViT)) for automated classification of vineyard canopy images int...

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Bibliographic Details
Main Authors: Ruben Íñiguez, Fikile Wolela, María Ignacia Gonzalez Pavez, Ignacio Barrio, Javier Tardáguila, Talitha Venter, Carlos Poblete-Echeverria
Format: Article
Language:English
Published: International Viticulture and Enology Society 2025-06-01
Series:OENO One
Subjects:
deep learning
vineyard phenology
YOLOv11
ResNet-34
Vision Transformer (ViT)
precision viticulture
Online Access:https://oeno-one.eu/article/view/9306
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