A novel dataset and deep learning object detection benchmark for grapevine pest surveillance

The use of new technologies such as microwaves (MW) arose in recent years as an efficient alternative to reduce the use of sulfur dioxide (SO2) and as a method for improving wines in terms of color and aroma [1, 2]. MW (non-ionizing electromagnetic waves with frequencies between 300 MHz and 300 GHz) have been widely applied in the food industry in order to reduce processing time and favor food preservation.

Water deficit and salinity are increasingly affecting the viticulture and wine industry. These two stresses are intimately related; understanding the physiological and metabolic responses of grapevines to water deficit, salinity and combined stress is critical for developing strategies to mitigate the nega- tive impacts of these stresses on wine grape production. These strategies can include selecting more tolerant grapevine cultivars and graft combinations, improving irrigation management, and using soil amendments to reduce the effects of salinity. For this purpose, understanding the response of grape- vine metabolism to altered water balance and salinity is of pivotal importance.

Enhancing the sensory profile of wine by the use of different microorganism has been always a challenge in winemaking. The aim of our work was to evaluate the impact of different fermentation schemes by using mixed and pure cultures of different Saccharomyces species to Sauvignon blanc wine chemical composition and sensory profile.

In the context of sustainable viticulture, modern and efficient techniques to determine water status are required to optimize irrigation practices. Proximal techniques such as thermography and spectroscopy have shown promising results. When these techniques are incorporated into mobile systems is possible to evaluate the water status on-the-go, offering the possibility to generate variability maps. However, in most cases, complex protocols of data acquisition and analysis are required. Also, the inherent physiological behaviour of the plants under certain water stress conditions needs to be considered. Therefore, the aim of this study was to evaluate the effect of scion-rootstock combinations on the performance of a predefined plant-based method based on proximal near-infrared (NIR) spectroscopy.

Franciacorta is a viticultural area which extends in the hills to the South of Iseo lake in Lombardy. It is particularly famous for the production of sparkling wines obtained mostly from Chardonnay and Pinot blanc and noir grapes. The name of this territory is of medieval origin and appeared for the first time in 1277 as “Franzacurta”, from the Latin “franchae curtes”, i.e. “tax-free” monasteries. It was geographically delimited in 1429, when it was a territory of the Republic of Venezia.