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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Detalhes bibliográficos

Principais autores:	Ruben Íñiguez, Fikile Wolela, María Ignacia Gonzalez Pavez, Ignacio Barrio, Javier Tardáguila, Talitha Venter, Carlos Poblete-Echeverria
Formato:	Artigo
Idioma:	Inglês
Publicado em:	International Viticulture and Enology Society 2025-06-01
coleção:	OENO One
Assuntos:	deep learning vineyard phenology YOLOv11 ResNet-34 Vision Transformer (ViT) precision viticulture
Acesso em linha:	https://oeno-one.eu/article/view/9306
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