Hyperspectral imaging and machine learning for monitoring grapevine physiology

Massif and the first sedimentary formations of the western aureole of the Paris Basin. If it is found all over the world (California, Israel, South Africa), it is in this region that it best asserts its identity. It is one of the most interesting grape varieties due to the variety and complexity of the wines it can produce. It can give very dry or very sweet, still or sparkling wines, fresh when young and sublime when ageing, expressing the characteristics of each vintage as much as those of the terroir. The Chenin is a faithful witness of its geographical, geological, pedological and climatic environment; he is the foil of the land. It has strong aptitudes for the production of sweet wines conditioned by overripe grapes often botrytised in the AOC Coteaux du Layon.Massif and the first sedimentary formations of the western aureole of the Paris Basin. If it is found all over the world (California, Israel, South Africa), it is in this region that it best asserts its identity. It is one of the most interesting grape varieties due to the variety and complexity of the wines it can produce. It can give very dry or very sweet, still or sparkling wines, fresh when young and sublime when ageing, expressing the characteristics of each vintage as much as those of the terroir. The Chenin is a faithful witness of its geographical, geological, pedological and climatic environment; he is the foil of the land. It has strong aptitudes for the production of sweet wines conditioned by overripe grapes often botrytised in the AOC Coteaux du Layon.

It is fundamental for wineries to know the potential yield of their vineyards as soon as possible for future planning of winery logistics. As such, non-invasive image-based methods are being investigated for early yield prediction. Many of these techniques have limitations that make it difficult to implement for practical use commercially. The aim of this study was to assess whether yield can be estimated using images taken in-field with a smartphone at different phenological stages. The accuracy of the method for predicting bunch weight at different phenological stages was assessed for seven different varieties.

Samples from 3 wine types were treated with a cationic exchange resin (7 lots) and stored for 8 years (47 samples). Forty-seven parameters were determined, including (1) important substrates with impact in white wine oxidation and (2) markers of oxidation. From group 1, sugars, elements, phenolic compounds, α-dicarbonyls and SO2 and from group 2, browning (A420), acetaldehyde, alkanals, furanic compounds were quantified.

Addressing the opportunities and challenges of genomics methods in grapevine (Vitis vinifera L.) requires the development of a comprehensive and accurate reference genome and annotation. We aimed to create a new gene annotation for the PN40024 grapevine reference genome by integrating the highly accurate and complete T2T assembly and the manually curated PN40024.v4 annotation. Here, we present a novel workflow to enhance the annotation of the T2T genome by incorporating past community input found in PN40024.v4. The pipeline’s containerization will improve the workflow’s reproducibility and flexibility, facilitating its inclusion as a shared workflow on the Grapedia portal, the grapevine genomics encyclopedia.

Grapevine downy mildew (GDM), caused by the obligate biotroph oomycete Plasmopara viticola, is one of the most destructive diseases in viticulture.