Hyperspectral imaging and machine learning for monitoring grapevine physiology

The increase in the temperature and the more severe water stress conditions, trends observed in recent years as a consequence of climate change, are leading a mismatch between the technological and phenolic maturity of grapes

Managing grapevines according to the practices of Precision Agriculture (PA), may prove to be an asset in the hands of the modern grape growers.

Performing accurate vineyard yield estimation is of upmost importance as it provides important benefits to the whole vine and wine industry. Recently, image-analysis approaches have been explored to address this issue however this approach has as main challenge the bunch occlusion, mostly by vegetation but also by neighboring bunches. The present work aims at assessing the magnitude of bunch occlusion by neighboring bunches and to evaluate its dependency on a selection of vegetative and reproductive vine parameters assessed at fruiting zone. Forty vine segments (1 m) of two vineyard plots of the white cultivars ‘Alvarinho’ and ‘Arinto’ were assessed for vegetative and reproductive features at fruiting zone and imaged with a 2D camera.

Berry composition at ripeness can be explained by many factors. This study was carried out from 2004 through 2011 in a 60 block network in the Yonne region, Burgundy.

Sulla base del tema assegnatomi è stata forte la tentazione di addentrarmi nel labirinto della regolamentazione comunitaria. Per buona pace degli intervenuti ho ritenuto, pero, poco utile una elencazione di numeri e riferimenti normativi che saranno brevemente riassunti in una tabella (TAB 1),