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1.
A Non-Invasive Method Based on Computer Vision for Grapevine Cluster Compactness Assessment Using a Mobile Sensing Platform under Field Conditions.
Sensors (Basel)
; 19(17)2019 Sep 02.
Artículo
en Inglés
| MEDLINE | ID: mdl-31480754
2.
Image analysis-based modelling for flower number estimation in grapevine.
J Sci Food Agric
; 97(3): 784-792, 2017 Feb.
Artículo
en Inglés
| MEDLINE | ID: mdl-27173452
3.
Data Mining and NIR Spectroscopy in Viticulture: Applications for Plant Phenotyping under Field Conditions.
Sensors (Basel)
; 16(2): 236, 2016 Feb 16.
Artículo
en Inglés
| MEDLINE | ID: mdl-26891304
4.
Estimation of total soluble solids in grape berries using a hand-held NIR spectrometer under field conditions.
J Sci Food Agric
; 96(9): 3007-16, 2016 Jul.
Artículo
en Inglés
| MEDLINE | ID: mdl-26399449
5.
Assessment of cluster yield components by image analysis.
J Sci Food Agric
; 95(6): 1274-82, 2015 Apr.
Artículo
en Inglés
| MEDLINE | ID: mdl-25041796
6.
Effects of UV exclusion on the physiology and phenolic composition of leaves and berries of Vitis vinifera cv. Graciano.
J Sci Food Agric
; 95(2): 409-16, 2015 Jan.
Artículo
en Inglés
| MEDLINE | ID: mdl-24820651
7.
Assessment of flower number per inflorescence in grapevine by image analysis under field conditions.
J Sci Food Agric
; 94(10): 1981-7, 2014 Aug.
Artículo
en Inglés
| MEDLINE | ID: mdl-24302287
8.
Phenolic composition of Tempranillo wines following early defoliation of the vines.
J Sci Food Agric
; 92(4): 925-34, 2012 Mar 15.
Artículo
en Inglés
| MEDLINE | ID: mdl-21968704
9.
Editorial: Resilience of grapevine to climate change: from plant physiology to adaptation strategies, volume II.
Front Plant Sci
; 14: 1268158, 2023.
Artículo
en Inglés
| MEDLINE | ID: mdl-37636123
10.
Development and Validation of a New Methodology to Assess the Vineyard Water Status by On-the-Go Near Infrared Spectroscopy.
Front Plant Sci
; 9: 59, 2018.
Artículo
en Inglés
| MEDLINE | ID: mdl-29441086
11.
Vineyard water status assessment using on-the-go thermal imaging and machine learning.
PLoS One
; 13(2): e0192037, 2018.
Artículo
en Inglés
| MEDLINE | ID: mdl-29389982
12.
On-The-Go Hyperspectral Imaging Under Field Conditions and Machine Learning for the Classification of Grapevine Varieties.
Front Plant Sci
; 9: 1102, 2018.
Artículo
en Inglés
| MEDLINE | ID: mdl-30090110
13.
Editorial: Resilience of grapevine to climate change: From plant physiology to adaptation strategies.
Front Plant Sci
; 13: 994267, 2022.
Artículo
en Inglés
| MEDLINE | ID: mdl-36017259
14.
Use of Visible and Short-Wave Near-Infrared Hyperspectral Imaging To Fingerprint Anthocyanins in Intact Grape Berries.
J Agric Food Chem
; 64(40): 7658-7666, 2016 Oct 12.
Artículo
en Inglés
| MEDLINE | ID: mdl-27653674
15.
Effects of ambient solar UV radiation on grapevine leaf physiology and berry phenolic composition along one entire season under Mediterranean field conditions.
Plant Physiol Biochem
; 109: 374-386, 2016 Dec.
Artículo
en Inglés
| MEDLINE | ID: mdl-27810677
16.
Support Vector Machine and Artificial Neural Network Models for the Classification of Grapevine Varieties Using a Portable NIR Spectrophotometer.
PLoS One
; 10(11): e0143197, 2015.
Artículo
en Inglés
| MEDLINE | ID: mdl-26600316
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