Implementation of hyperspectral image analysis for evaluating table grape quality on bunch and berry level

In 2022, wine production was estimated at around 260 million hl. This high production rate implies to generate a large amount of by-products, which include grape pomace, grape stalks and wine lees. It is estimated that processing 100 tons of grapes leads to ~ 22 tons of by-products from which ~ 6 tons are lees [1]. Wine lees are a sludge-looking material mostly made of dead and living yeast cells, yeast debris and other particles that precipitate at the bottom of wine tanks after alcoholic fermentation. Unlike grape pomace or grape stalks, few strategies have been proposed for the recovery and valorisation of wine less [2].

It is a well-established fact that premature oxidation is noxious for wine aromatic quality and longevity. Although some oxidation-related aroma molecules have been previously identified, there are not works carrying out systematic research about the changes in the profiles of odour-active volatiles during wine oxidation.

Planting grape varieties with several resistance loci towards powdery and downy mildew reduces the use of fungicides significantly. These fungus resistant or PIWI varieties (acronym of German Pilzwiderstandsfähig) contribute significantly to the 50% pesticide reduction goal, set by the European Green Deal for 2030. However, wine growers hesitate to plant PIWIs as they lack experience in vinification and are uncertain, how consumer accept and buy wines from these yet mostly unknown varieties. Grapes from four white and three red PIWI varieties were vinified in three vintages to obtain four diffe-rent white and red wine styles, respectively plus one rosé.

The vineyard of Bergerac, a guaranteed vintage, is situated in the mid-Lot valley, which has siliceous terraced rows on its hillsides, and on its bordering plateaux, composed of limestone and clay of the tertiary geological eras.

Grapevine yield is a key indicator to assess the impacts of climate change and the relevance of adaptation strategies in a vineyard landscape. At this scale, a yield model should use a number of parameters and input data in relation to the information available and be able to reproduce vineyard management decisions (e.g. soil and canopy management, irrigation). In this study, we used data from six experimental sites in Southern France (cv. Syrah) to calibrate a model of grapevine yield limited by water constraint (GraY). Each yield component (bud fertility, number of berries per bunch, berry weight) was calculated as a function of the soil water availability simulated by the WaLIS water balance model at critical phenological phases. The model was then evaluated in 10 grapegrowers’ plots, covering a diversity of biophysical and technical contexts (soil type, canopy size, irrigation, cover crop). We identified three critical periods for yield formation: after flowering on the previous year for the number of bunches and berries, around pre-veraison and post-veraison of the same year for mean berry weight. Yields were simulated with a model efficiency (EF) of 0.62 (NRMSE = 0.28). Bud fertility and number of berries per bunch were more accurately simulated (EF = 0.90 and 0.77, NRMSE = 0.06 and 0.10, respectively) than berry weight (EF = -0.31, NRMSE = 0.17). Model efficiency on the on-farm plots reached 0.71 (NRMSE = 0.37) simulating yields from 1 to 8 kg/plant. The GraY model is an original model estimating grapevine yield evolution on the basis of water availability under future climatic conditions.  It allows to evaluate the effects of various adaptation levers such as planting density, cover crop management, fruit/leaf ratio, shading and irrigation, in various production contexts.