Effect of ozone application for low-input postharvest dehydration of wine grapes 

The climate has warmed over the past century or more bringing about changes in numerous aspects in both earth and human systems

Aim: Pests have a high impact on yield and grape quality in viticulture. An objective and rapid detection of pests under field conditions is needed. New sensing technologies and artificial intelligence could be used for pests detection in digital viticulture. The aim of this work was to apply computer vision and deep learning techniques for automatic detection of spider mite symptoms in grapevine under field conditions. 

Grapevine is a crop of great economic importance for several countries. The intensification of grapevine production has mostly been sustained by the increasing use of water resources at the expense of the environmental water balance. Moreover, in the last decades, climate change and the consequent expansion of drought have further compromised water availability, making current agricultural systems even more fragile both ecologically and economically. Recently, many research groups have highlighted the important role of endophytes in facilitating plant growth under optimal or stressful conditions. Within the framework of the PRIMA project, we aim to investigate the possible exploitation of the natural endophyte biodiversity as a sustainable tool to make grapevine plants more resilient to water deficit environmental conditions.

In the context of climate change and for the sustainable exploitation of Mediterranean vineyards, it is necessary to use new strategies to adapt to the new climatic conditions.

Climatic variability studies at fine scale have been developed in recent years with the reduction of material cost and the development of competitive miniaturized sensors. This work is forming part the LIFE-ADVICLIM project, of which one of the objectives is to model spatial temperature variability at vineyard scale. In the Bordeaux pilot site, a large network of data loggers has been set up to record temperature close to the vine canopy. The reduced distance between plant foliage and measurement equipment raises specific issues and leads to an increased rate of outliers compared to data retrieved from classical weather stations. Some of these were detected during data analysis, but others could not be easily identified. The present study aims to address the issue of data quality control and provide recommendations for data processing in climatic studies at fine scale.