High resolution remote sensing for mapping intra-block vine vigour heterogeneity

In this work, these four red varieties were characterized in terms of phenolic composition. Thus, the anthocyanin accumulation and the extractability evolution during ripening were compared.

Context and purpose of the study. Red Blotch disease, an affliction caused by the Grapevine red blotch-associated virus (GRBaV), represents a formidable challenge for grape growers and winemakers in prominent viticultural regions around the world.

The influences of quality compost A+ and of a commercial organic fertilizer based on dry mash from bioethanol production, blackstrap molasses, vinasse, PNC (potato nitrogen concentrate) and CSL (corn steep liquor) on the humus content, on the mineral nitrogen content in the soil, in the must and in the vine leaves, on pruning wood

To assess the relationship between the reported low-stability of Tannat colour during wine storage and its pigment composition and evolution

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.