Publicaciones en colaboración con investigadores/as de Instituto de Ciencias de la Vid y del Vino (17)

2024

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

    Computers and Electronics in Agriculture, Vol. 226

2023

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

    Biosystems Engineering, Vol. 229, pp. 167-178

2021

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

    Irrigation Science, Vol. 39, Núm. 4, pp. 457-468

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

    Computers and Electronics in Agriculture, Vol. 182

  3. Smart applications and digital technologies in viticulture: A review

    Smart Agricultural Technology, Vol. 1

2019

  1. Ground based hyperspectral imaging for extensive mango yield estimation

    Computers and Electronics in Agriculture, Vol. 157, pp. 126-135

  2. Spectral filter design based on in-field hyperspectral imaging and machine learning for mango ripeness estimation

    Computers and Electronics in Agriculture, Vol. 164

2018

  1. Development and validation of a new methodology to assess the vineyard water status by on-the-go near infrared spectroscopy

    Frontiers in Plant Science, Vol. 9

  2. In field quantification and discrimination of different vineyard water regimes by on-the-go NIR spectroscopy

    Biosystems Engineering, Vol. 165, pp. 47-58

  3. On-the-go hyperspectral imaging under field conditions and machine learning for the classification of grapevine varieties

    Frontiers in Plant Science, Vol. 9

  4. Vineyard water status assessment using on-the-go thermal imaging and machine learning

    PLoS ONE, Vol. 13, Núm. 2

2017

  1. In-field assessment of grapevine water status using a portable NIR spectrophotometer

    Acta Horticulturae, Vol. 1150, pp. 167-172

  2. Non-destructive assessment of grapevine water status in the field using a portable NIR spectrophotometer

    Journal of the Science of Food and Agriculture, Vol. 97, Núm. 11, pp. 3772-3780

2016

  1. Data mining and NIR spectroscopy in viticulture: Applications for plant phenotyping under field conditions

    Sensors, Vol. 16, Núm. 2

2015

  1. Grapevine flower estimation by applying artificial vision techniques on images with uncontrolled scene and multi-model analysis

    Computers and Electronics in Agriculture, Vol. 119, pp. 92-104

  2. Landmark-based music recognition system optimisation using genetic algorithms

    Multimedia Tools and Applications, Vol. 75, Núm. 24, pp. 16905

  3. Support vector machine and artificial neural network models for the classification of grapevine varieties using a portable NIR spectrophotometer

    PLoS ONE, Vol. 10, Núm. 11