Monitoring of ripening and yield of vineyards in Nemea region using UAV

Grapevine leafroll associated virus (GLRaV) infections deteriorate vine physiological performance and cause high losses of yield and fruit quality

Modern winemakers must ensure effective alcoholic fermentation without losing the intrinsic biodiversity of the different oenological contexts. In this sense, the population of saccharomyces cerevisiae characteristic of wineries that traditionally do not use selected yeasts can represent an interesting reservoir of biodiversity.

Rootstocks are gaining importance in viticulture as a strategy to combat abiotic challenges, as well as enhancing scion physiology and attributes. Therefore, understanding how the rootstock affects photosynthesis is insightful for genetic improvement of either genotype in the grafted grapevines. Photosynthetic parameters such as maximum rate of carboxylation of RuBP (Vcmax) and the maximum rate of electron transport driving RuBP regeneration (Jmax) have been identified as ideal targets for breeding and genetic studies. However, techniques used to directly measure these photosynthetic parameters are limited to the single leaf level and are time-consuming measurements.

L’identification des produits par le nom de la ville, de la région, de la province d’origine d’un produit tend aujourd’hui à se développer partout dans le monde et notamment dans le secteur agro-alimentaire, mais aussi dans les secteurs des produits artisanaux.

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.