Haplotype-Resolved genome assembly of the Microvine

Flavescence dorée (FD) poses a significant threat to grapevine health, with the American grapevine leafhopper, Scaphoideus titanus, serving as the primary vector.

The objective of this study was to develop a methodology capable of modeling the effect of viticultural climate on wine sensory characteristics.

The grapes producing and wine making regions are différent in their use of agricultural, industrial or agroindustrial means. These means are quite often very original and/or specialised; and lately are also quite competitive. Such means are being defined with increased accuracy in the delimitation and definition of its characteristics (Paneque et al., 1996 a). Human action together with other Elements and Agents involved in the vine growing production (Reyner, 1989) over these means lead to agronomic systems with important characteristics. Finally, the transformation of the vine growing production, through different technologies (Fleet, 1992), results in the creation of products with a different acceptance and economical value in the market.

Climate change is a great environmental challenge with large impact on the Wine and sprakling wine industry. Heat waves and dryness cause frequent sunburn damage in white grapes

Currently, the main goal of agriculture is to promote the resilience of agricultural systems in a sustainable way through the improvement of use efficiency of farm resources, increasing crop yield and quality under climate change conditions. This last is expected to drastically modify plant growth, with possible negative effects, especially in arid and semi-arid regions of Europe on the viticultural sector. In this context, the monitoring of spatial behavior of grapevine during the growing season represents an opportunity to improve the plant management, winegrowers’ incomes, and to preserve the environmental health, but it has additional costs for the farmer. Nowadays, UAS equipped with a VIS-NIR multispectral camera (blue, green, red, red-edge, and NIR) represents a good and relatively cheap solution to assess plant status spatial information (by means of a limited set of spectral vegetation indices), representing important support in precision agriculture management during the growing season. While differences between UAS-based multispectral imagery and point-based spectroscopy are well discussed in the literature, their impact on plant status estimation by vegetation indices is not completely investigated in depth. The aim of this study was to assess the performance level of UAS-based multispectral (5 bands across 450-800nm spectral region with a spatial resolution of 5cm) imagery, reconstructed high-resolution satellite (Sentinel-2A) multispectral imagery (13 bands across 400-2500 nm with spatial resolution of <2 m) through Convolutional Neural Network (CNN) approach, and point-based field spectroscopy (collecting 600 wavelengths across 400-1000 nm spectral region with a surface footprint of 1-2 cm) in a plant status estimation application, and then, using Bayesian regularization artificial neural network for leaf chlorophyll content (LCC) and plant water status (LWP) prediction. The test site is a Greco vineyard of southern Italy, where detailed and precise records on soil and atmosphere systems, in-vivo plant monitoring of eco-physiological parameters have been conducted.