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)

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)