María-Paz Diago

According to our database, María-Paz Diago authored at least 11 papers between 2012 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Multi-sensor spectral fusion to model grape composition using deep learning.

Salvador Gutiérrez, Juan Fernández-Novales, Teresa Garde-Cerdán, Sandra Marín-San Román, Javier Tardáguila, María-Paz Diago

Inf. Fusion, November, 2023

2021

Monitoring and Mapping Vineyard Water Status Using Non-Invasive Technologies by a Ground Robot.

Juan Fernández-Novales, Verónica Sáiz-Rubio, Ignacio Barrio, Francisco Rovira-Más, Andrés Cuenca-Cuenca, Fernando Alves, Joana Valente, Javier Tardáguila, María-Paz Diago

Remote. Sens., 2021

2020

Automated grapevine flower detection and quantification method based on computer vision and deep learning from on-the-go imaging using a mobile sensing platform under field conditions.

Fernando Palacios, Gloria Bueno, Jesús Salido, María-Paz Diago, Inés Hernández, Javier Tardáguila

Comput. Electron. Agric., 2020

2019

A Non-Invasive Method Based on Computer Vision for Grapevine Cluster Compactness Assessment Using a Mobile Sensing Platform under Field Conditions.

Fernando Palacios, María-Paz Diago, Javier Tardáguila

Sensors, 2019

2018

Automated early yield prediction in vineyards from on-the-go image acquisition.

Arturo Aquino, Borja Millán, María-Paz Diago, Javier Tardáguila

Comput. Electron. Agric., 2018

vitisBerry: An Android-smartphone application to early evaluate the number of grapevine berries by means of image analysis.

Arturo Aquino, Ignacio Barrio, María-Paz Diago, Borja Millán, Javier Tardáguila

Comput. Electron. Agric., 2018

2016

Data Mining and NIR Spectroscopy in Viticulture: Applications for Plant Phenotyping under Field Conditions.

Salvador Gutiérrez, Javier Tardáguila, Juan Fernández-Novales, María-Paz Diago

Sensors, 2016

2015

vitisFlower^®: Development and Testing of a Novel Android-Smartphone Application for Assessing the Number of Grapevine Flowers per Inflorescence Using Artificial Vision Techniques.

Arturo Aquino, Borja Millán, Daniel Gaston, María-Paz Diago, Javier Tardáguila

Sensors, 2015

Using RPAS Multi-Spectral Imagery to Characterise Vigour, Leaf Development, Yield Components and Berry Composition Variability within a Vineyard.

Clara Rey-Caramés, María-Paz Diago, M. Pilar Martín, Agustin Lobo, Javier Tardáguila

Remote. Sens., 2015

2013

Using an Automatic Resistivity Profiler Soil Sensor On-The-Go in Precision Viticulture.

Roberta Rossi, Alessio Pollice, María-Paz Diago, Manuel Oliveira, Borja Millán, Giovanni Bitella, Mariana Amato, Javier Tardáguila

Sensors, 2013

2012

Grapevine Yield and Leaf Area Estimation Using Supervised Classification Methodology on RGB Images Taken under Field Conditions.

María-Paz Diago, Christian Correa, Borja Millán, Pilar Barreiro, Constantino Valero, Javier Tardáguila

Sensors, 2012