Javier Tardaguila

Full Professor of Precision Viticulture

University of La Rioja

Spain

Research Interests

Explore related searches

Professors in Spain

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Javier Tardaguila is a Full Professor of Precision Viticulture at the University of La Rioja in Spain. His research focuses on the application of deep learning and computer vision techniques in viticulture, including the early detection of downy mildew and grape bunches, as well as vineyard water status monitoring. His recent publications highlight the use of advanced imaging and machine learning methods to enhance grapevine management strategies.

Articles (19)

Year:

2025

Year:

2025

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2021

Year:

2021

Year:

2021

Year:

2021

Year:

2021

Year:

2020

Collaborators (4)

Salvador Gutiérrez

Lecturer

Universidad de Granada

SPAIN

View details

Juan Fernández Novales

Profesor Ayudante Doctor

University of La Rioja

SPAIN

View details

Pedro Melo-Pinto

Universidade de Trás-os-Montes e Alto Douro

PORTUGAL

View details

Gloria Bueno

University of Castilla-La Mancha

SPAIN

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Javier Tardaguila

Research Interests

Explore related searches

Contact this professor

About

Articles (19)

Impact of Phenological and Lighting Conditions on Early Detection of Grapevine Inflorescences and Bunches Using Deep Learning

Early detection of downy mildew in vineyards using deep neural networks for semantic segmentation

In-field disease symptom detection and localisation using explainable deep learning: Use case for downy mildew in grapevine

Image analysis with deep learning for early detection of downy mildew in grapevine

Deep learning modelling for non-invasive grape bunch detection under diverse occlusion conditions

NIR attribute selection for the development of vineyard water status predictive models

Early yield prediction in different grapevine varieties using computer vision and machine learning

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

Evolutionary conditional GANs for supervised data augmentation: The case of assessing berry number per cluster in grapevine

Assessment of downy mildew in grapevine using computer vision and fuzzy logic. Development and validation of a new method

Low-Cost Electronic Nose for Wine Variety Identification through Machine Learning Algorithms

Deep learning and computer vision for assessing the number of actual berries in commercial vineyards

Combination of multispectral imagery, environmental data and thermography for on-the-go monitoring of the grapevine water status in commercial vineyards

Artificial intelligence and novel sensing technologies for assessing downy mildew in grapevine

Assessing and mapping vineyard water status using a ground mobile thermal imaging platform

Deep learning for the differentiation of downy mildew and spider mite in grapevine under field conditions

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

Impact of Leaf Occlusions on Yield Assessment by Computer Vision in Commercial Vineyards

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

Collaborators (4)

Salvador Gutiérrez

Juan Fernández Novales

Pedro Melo-Pinto

Gloria Bueno

Make your next move.