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

A. Aquino, B. Millán, D. Gaston, M. Diago, and J. Tardáguila. Sensors, 15 (9): 21204-21218 (2015)

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Maria Paz Arzaga

Maria-Paz Weisshaar

María Paz Squella Padilla

Paz Schamber

Gerardo Paz

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A Non-Invasive Method Based on Computer Vision for Grapevine Cluster Compactness Assessment Using a Mobile Sensing Platform under Field Conditions.F. Palacios, M. Diago, and J. Tardáguila. Sensors, 19 (17): 3799 (2019)Using an Automatic Resistivity Profiler Soil Sensor On-The-Go in Precision Viticulture.R. Rossi, A. Pollice, M. Diago, M. Oliveira, B. Millán, G. Bitella, M. Amato, and J. Tardáguila. Sensors, 13 (1): 1121-1136 (2013)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.F. Palacios, G. Bueno, J. Salido, M. Diago, I. Hernández, and J. Tardáguila. Comput. Electron. Agric., (2020)Grapevine Yield and Leaf Area Estimation Using Supervised Classification Methodology on RGB Images Taken under Field Conditions.M. Diago, C. Correa, B. Millán, P. Barreiro, C. Valero, and J. Tardáguila. Sensors, 12 (12): 16988-17006 (2012)vitisFlower®: Development and Testing of a Novel Android-Smartphone Application for Assessing the Number of Grapevine Flowers per Inflorescence Using Artificial Vision Techniques.A. Aquino, B. Millán, D. Gaston, M. Diago, and J. Tardáguila. Sensors, 15 (9): 21204-21218 (2015)Data Mining and NIR Spectroscopy in Viticulture: Applications for Plant Phenotyping under Field Conditions.S. Gutiérrez, J. Tardáguila, J. Fernández-Novales, and M. Diago. Sensors, 16 (2): 236 (2016)vitisBerry: An Android-smartphone application to early evaluate the number of grapevine berries by means of image analysis.A. Aquino, I. Barrio, M. Diago, B. Millán, and J. Tardáguila. Comput. Electron. Agric., (2018)Automated early yield prediction in vineyards from on-the-go image acquisition.A. Aquino, B. Millán, M. Diago, and J. Tardáguila. Comput. Electron. Agric., (2018)Monitoring and Mapping Vineyard Water Status Using Non-Invasive Technologies by a Ground Robot.J. Fernández-Novales, V. Sáiz-Rubio, I. Barrio, F. Rovira-Más, A. Cuenca-Cuenca, F. Alves, J. Valente, J. Tardáguila, and M. Diago. Remote. Sens., 13 (14): 2830 (2021)Using RPAS Multi-Spectral Imagery to Characterise Vigour, Leaf Development, Yield Components and Berry Composition Variability within a Vineyard.C. Rey-Caramés, M. Diago, M. Martín, A. Lobo, and J. Tardáguila. Remote. Sens., 7 (11): 14458-14481 (2015)