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)

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)

Grapevine flower estimation by applying artificial vision techniques on images with uncontrolled scene and multi-model analysis. - Arturo Aquino; Borja Millán; Salvador Gutiérrez; Javier Tardáguila (Comput. Electron. Agric., 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)

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)

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)

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)

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)

On-the-Go Grapevine Yield Estimation Using Image Analysis and Boolean Model. - Borja Millán; Santiago Velasco-Forero; Arturo Aquino; Javier Tardáguila (J. Sensors, 2018)

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)

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)

Deep learning for the differentiation of downy mildew and spider mite in grapevine under field conditions. - Salvador Gutiérrez; Inés Hernández; Sara Ceballos; Ignacio Barrio; Ana Díez Navajas; Javier Tardáguila (Comput. Electron. Agric., 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)

Evolutionary conditional GANs for supervised data augmentation: The case of assessing berry number per cluster in grapevine. - Salvador Gutiérrez; Javier Tardáguila (Appl. Soft Comput., 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, 2023)

In-field disease symptom detection and localisation using explainable deep learning: Use case for downy mildew in grapevine. - Inés Hernández; Salvador Gutiérrez; Ignacio Barrio; Rubén Íñiguez; Javier Tardáguila (Comput. Electron. Agric., 2024)

Deep learning modelling for non-invasive grape bunch detection under diverse occlusion conditions. - Rubén Íñiguez; Salvador Gutiérrez; Carlos Poblete-Echeverría; Inés Hernández; Ignacio Barrio; Javier Tardáguila (Comput. Electron. Agric., 2024)

Small data deep learning methodology for in-field disease detection. - David Herrera-Poyatos; Jacinto Domínguez-Rull; Rosana Montes; Inés Hernández; Ignacio Barrio; Carlos Poblete-Echeverría; Javier Tardáguila; Francisco Herrera; Andrés Herrera-Poyatos (CoRR, 2024)

On-the-go assessment of the grapevine trunk's diameter: a comparison of different convolutional neural networks. - Alessandro Zanchin; Inés Hernández; Rubén Íñiguez; Marco Sozzi; Diego Tomasi; Francesco Marinello; Javier Tardáguila (Comput. Electron. Agric., 2025)