Javier Tardáguila Laso-rekin lankidetzan egindako argitalpenak (31)

2024

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

    Computers and Electronics in Agriculture, Vol. 226

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

    Scientia Horticulturae, Vol. 331

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

    Applied Soft Computing, Vol. 147

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

    Information Fusion, Vol. 99

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

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

2022

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

    Oeno One, Vol. 56, Núm. 3, pp. 41-53

2021

  1. Aplicación de la visión por computador para evaluar la canopy en viñedos comerciales

    La Semana vitivinícola, Núm. 3589, pp. 468-471

  2. Aplicación de la visión por computador para evaluar la canopy en viñedos comerciales

    La Semana vitivinícola, Núm. 3605, pp. 1991-1994

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

    Horticulturae, Vol. 7, Núm. 5

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

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

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

    Computers and Electronics in Agriculture, Vol. 182

  6. Estimación de la producción de uva del viñedo mediante visión artificial

    La Semana vitivinícola, Núm. 3605, pp. 1995-1997

  7. Estimación de la producción de uva del viñedo mediante visión artificial

    La Semana vitivinícola, Núm. 3589, pp. 472-474

  8. Impact of leaf occlusions on yield assessment by computer vision in commercial vineyards

    Agronomy, Vol. 11, Núm. 5

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

    Smart Agricultural Technology, Vol. 1

2019

  1. Hyperspectral imaging application under field conditions: Assessment of the spatio-temporal variability of grape composition within a vineyard

    Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019

  2. On-The-Go VIS + SW - NIR Spectroscopy as a Reliable Monitoring Tool for Grape Composition within the Vineyard

    Molecules (Basel, Switzerland), Vol. 24, Núm. 15

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