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

This article is an original research article published in cooperation with the 23rd GiESCO International Conference, July 21-27, 2025, hosted by the Hochschule Geisenheim University in Geisenheim, Germany

Bibliographic Details
Authors: Íñiguez, Rubén, Wolela, Fikile, Gonzalez Pavez, María Ignacia, Barrio Fernández, Ignacio, Tardáguila, Javier, Venter, Talitha, Poblete-Echeverría, Carlos
Format: article
Status:Published version
Publication Date:2025
Country:España
Institution:Consejo Superior de Investigaciones Científicas (CSIC)
Repository:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/400383
Online Access:http://hdl.handle.net/10261/400383
https://api.elsevier.com/content/abstract/scopus_id/105008830286
Access Level:Open access
Keyword:Automated classification
Deep learning
GiESCO 2025
Precision viticulture
ResNet-34
Vineyard phenology
Vision Transformer (ViT)
YOLOv11
Description
Summary:This article is an original research article published in cooperation with the 23rd GiESCO International Conference, July 21-27, 2025, hosted by the Hochschule Geisenheim University in Geisenheim, Germany

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