Stabilità dei caratteri fenotipici dl alcune cv in diversi pedopaesaggi friulani. Applicazione del metodo nella caratterizzazione viticola del comprensorio DOC “Friuli-Grave”

Damage during the budbreak period due to spring season frosts remains one of the most significant weather-related challenges to viticulture around the world. For example, in 2021, €2bn of estimated damage was reported in france while >50% of vineyards were badly affected in the UK in 2017.

Sunburn is a physiological disorder that leads to yield and quality losses in a range of fruits such as grapes and apples. It affects the visual appearance and the composition of the fruit, leading to irreversible changes and ultimately, cell death in extreme situations.

The wine sector is undergoing considerable transformation, particularly as a result of climate change and increasing consumer expectations for quality products, in a globalised and increasingly competitive market.

Typically, subjective, and visual methods are used by grape growers to assess harvest maturity. These methods may not accurately represent the maturity of an entire vineyard – especially if extensive and representative sampling was not used. New technologies have been investigated for improved harvest management decisions. Spectroscopy methods utilizing the near-infrared region of the light spectrum is one such technology investigated as an alternative to classic methods and particularly the application of hyperspectral imaging (HSI) has recently gained attention in research. HIS is a spectroscopic technique that obtains hundreds of images at different wavelengths collecting spectral data for each pixel in the sample i.e., providing both spectral and spatial data.

the phenological evolution is a crucial aspect of grapevine growth and development. Accurate detection of phenological stages can improve vineyard management, leading to better crop yield and quality traits. However, traditional methods of phenological tracking such as on-site observations are time-consuming and labour-intensive. This work proposes a scalable data-driven method to automatically detect key phenological stages of grapevines using satellite data. Our approach applies to vast areas because it solely relies on open and satellite data having global coverage without requiring any in-field data from weather stations or other sensors making the approach extensible to other areas.