Publikationen (104) Publikationen, an denen Forscher/innen teilgenommen haben

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

  4. Use of the hyperspectral imaging to estimate the volatile composition of Tempranillo grape berries during ripening

    Scientia Horticulturae, Vol. 337

2023

  1. Application of near-infrared spectroscopy for the estimation of volatile compounds in Tempranillo Blanco grape berries during ripening

    Journal of the Science of Food and Agriculture, Vol. 103, Núm. 13, pp. 6317-6329

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

    Precision Agriculture, Vol. 24, Núm. 2, pp. 407-435

  3. Editorial: Resilience of grapevine to climate change: from plant physiology to adaptation strategies, volume II

    Frontiers in Plant Science

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

    Applied Soft Computing, Vol. 147

  5. Inteligencia artificial y visión por ordenador para evaluar los componentes del rendimiento de la vid en viñedos comerciales

    BIO Web of Conferences: 44th World Congress of Vine and Wine

  6. Monitorization of Varietal Aroma Composition Dynamics during Ripening in Intact Vitis vinifera L. Tempranillo Blanco Berries by Hyperspectral Imaging

    Journal of Agricultural and Food Chemistry, Vol. 71, Núm. 5, pp. 2616-2627

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

    Information Fusion, Vol. 99

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

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

  9. Towards the automation of NIR spectroscopy to assess vineyard water status spatial–temporal variability from a ground moving vehicle

    Scientific Reports, Vol. 13, Núm. 1

  10. Using artificial intelligence (AI) for grapevine disease detection based on images

    BIO Web of Conferences: 44th World Congress of Vine and Wine

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

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

    European Journal of Agronomy, Vol. 140

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

    Biosystems Engineering, Vol. 218, pp. 175-188

  4. Editorial: Resilience of grapevine to climate change: From plant physiology to adaptation strategies

    Frontiers in Plant Science

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

    Agronomy, Vol. 12, Núm. 11

  6. Message from the Technical Program Chairs

    2022 IEEE Workshop on Metrology for Agriculture and Forestry, MetroAgriFor 2022 - Proceedings