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
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/400383 |
| Acceso en línea: | http://hdl.handle.net/10261/400383 https://api.elsevier.com/content/abstract/scopus_id/105008830286 |
| Access Level: | acceso abierto |
| Palabra clave: | Automated classification Deep learning GiESCO 2025 Precision viticulture ResNet-34 Vineyard phenology Vision Transformer (ViT) YOLOv11 |
| Sumario: | 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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