Xylem vessel blockages in grape pedicel growing in tropical climate observed by microtomography

The interaction between mannoproteins and wine phenolic compounds is a subject of great interest as some studies show the possible impact in color stability and an improvement in the sensory characteristics namely the reduction of red wine astringency.

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.

An in-depth knowledge on the conditions that trigger Botrytis disease and the microbial community associated with the susceptibility/resistance to it could led to the anticipation and response to the Botrytis emergence and severity. Therefore, the present study pretends to establish links between biotic and abiotic factors and the presence/abundance of B. cinerea.

Temperature fluctuations can significantly affect the chemical composition of wine and in turn its taste and aromas.

Aims: The optimization of nitrogen use efficiency (NUE, i.e. uptake, assimilation and partitioning) is a solution towards the sustainable production of premium wines, while reducing fertilization and environmental impact. The influence of crop load on the accumulation of N compounds in fruits is still poorly understood. The present study assesses the impacts of bunch thinning on NUE and the consequences on the free amino N (FAN) profile in fruits.