Cargando…
A Non-Invasive Method Based on Computer Vision for Grapevine Cluster Compactness Assessment Using a Mobile Sensing Platform under Field Conditions
Grapevine cluster compactness affects grape composition, fungal disease incidence, and wine quality. Thus far, cluster compactness assessment has been based on visual inspection performed by trained evaluators with very scarce application in the wine industry. The goal of this work was to develop a...
Autores principales: | Palacios, Fernando, Diago, Maria P., Tardaguila, Javier |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6749308/ https://www.ncbi.nlm.nih.gov/pubmed/31480754 http://dx.doi.org/10.3390/s19173799 |
Ejemplares similares
-
On-The-Go Hyperspectral Imaging Under Field Conditions and Machine Learning for the Classification of Grapevine Varieties
por: Gutiérrez, Salvador, et al.
Publicado: (2018) -
Grapevine Yield and Leaf Area Estimation Using Supervised Classification Methodology on RGB Images Taken under Field Conditions
por: Diago, Maria-Paz, et al.
Publicado: (2012) -
vitisFlower(®): Development and Testing of a Novel Android-Smartphone Application for Assessing the Number of Grapevine Flowers per Inflorescence Using Artificial Vision Techniques
por: Aquino, Arturo, et al.
Publicado: (2015) -
Support Vector Machine and Artificial Neural Network Models for the Classification of Grapevine Varieties Using a Portable NIR Spectrophotometer
por: Gutiérrez, Salvador, et al.
Publicado: (2015) -
Data Mining and NIR Spectroscopy in Viticulture: Applications for Plant Phenotyping under Field Conditions †
por: Gutiérrez, Salvador, et al.
Publicado: (2016)