Hyperspectral imaging and machine learning for monitoring grapevine physiology

The Spotted lanternfly (SLF), first detected in the U.S. in 2014, is an invasive phloem-feeding planthopper that poses a growing threat to grape and wine production in the U.S. In Pennsylvania, where it was first detected, reductions in grapevine production and fruit quality have been reported by commercial growers. Recent advances have begun to elucidate how SLF affects grapevine physiology and resource allocation, but no research has identified how SLF affects wine chemical composition and quality. Documented reductions in fruit sugar allocation due to heavy SLF phloem-feeding may have downstream effects on wine fermentation dynamics. Additionally, secondary metabolic responses stimulated by SLF may also influence berry chemical composition. The present study investigated SLF-mediated effects on wine composition through analysis of the volatile composition of wines produced from white- and red-fruited varieties of different Vitis parentage (e.g., Vitis vinifera vs. interspecific hybrids) following prolonged exposure to adult SLF phloem-feeding.

The color of a red wine is one of the most important parameters of its quality, giving much information on its status, such as the grape variety used or the winemaking style. As the result of a complex equilibrium between different forms of anthocyanins and polymerization reactions which occur over the course of time, color can also serve as an indication of a wines’ age. For this purpose the “chemical age” i and ii indexes have been introduced by Somers in 1977. The chemical age index i measures the color absorbance after the addition of acetaldehyde while chemical index ii provides an indication of how much of the total red pigments are resistant to SO2 bleaching.

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.

Le vin est un des produits finis que l’on obtient à partir de raisins. La vigne réagit à de nombreux facteurs environnementaux et son comportement est directement influencé par les pratiques culturales

The SUSTAIN project aims at assessing the soil organic carbon (SOC) stock and vulnerability in vineyard in a climate change scenario.