Machines and fire: developing a rapid detection system for grapevine smoke contamination using NIR spectroscopy and machine learning modelling

Ampelography is aimed at describing the vine according to several characteristics, such as morphology, agronomic aptitudes, technological potential, and genetics. The description of varieties and species of vitis has long been the subject of numerous scientific and technical studies by eminent specialists for a long time, which have led the OIV to publish in 1983 the “descriptor list for grape varieties and vitis species”, a milestone among the OIV worldwide recognised codes.

Optimizing the use of water and improving irrigation strategies has become increasingly important in most winegrowing countries due to the consequences of climate change, which are leading to more frequent droughts, heat waves, or alteration of precipitation patterns. Optimized irrigation scheduling can only be based on a reliable knowledge of the vineyard water status.

In this context, this work aims at the development of a novel methodology, using a contactless, miniaturized, low-cost NIR spectral tool to monitor (on-the-go) the vineyard water status variability. On-the-go spectral measurements were acquired in the vineyard using a NIR micro spectrometer, operating in the 900–1900 nm spectral range, from a ground vehicle moving at 3 km/h. Spectral measurements were collected on the northeast side of the canopy across four different dates (July 8th, 14th, 21st and August 12th) during 2021 season in a commercial vineyard (3 ha). Grapevines of Vitis vinifera L. Graciano planted on a VSP trellis were monitored at solar noon using stem water potential (Ψs) as reference indicators of plant water status. In total, 108 measurements of Ψs were taken (27 vines per date).

Calibration and prediction models were performed using Partial Least Squares (PLS) regression. The best prediction models for grapevine water status yielded a determination coefficient of cross-validation (r2cv) of 0.67 and a root mean square error of cross-validation (RMSEcv) of 0.131 MPa. This predictive model was employed to map the spatial variability of the vineyard water status and provided useful, practical information towards the implementation of appropriate irrigation strategies. The outcomes presented in this work show the great potential of this low-cost methodology to assess the vineyard stem water potential and its spatial variability in a commercial vineyard.

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.

La région de production de la Gambellara et Recioto di Gambellara DOC (variété Garganega), tout en n’intéressant qu’une surface limitée, présente une certaine variabilité de milieu due à la morphologie du territoire (colline et plaine), à l’état actuel des sols et aux variations climatiques entre les différents sites. Pour les années 2001, 2002

Nitrogen (N2) is critical in grape berries, especially in organic wine making. After intake, N2 follows various metabolic and allocation routes and, from veraison, partly reallocates into berries. Water deficit affects the N2 nutrition due to a poor diffusion in soil solution and vascular mobilisation. Also, affects photosynthesis and the energy needed for metabolism, whose extent would depend on the stomatal sensitivity of the plant. We have assessed the effect of a moderate water deficit from pea size, in 3 years old field grown potted plants of Chardonnay (CH) and Cabernet Sauvignon (CS), differing in stomatal sensitivity, on the N2 status of plant parts. Water deficit reduced photosynthesis, leaf area and fresh and dry plant mass along the season, but up to a higher extent in CS.