Sustainablity of vineyards in the Priorat region (NE Spain)

Context and purpose of the study. The growing need for sustainable solutions in viticulture has led to increased interest in biostimulants that can enhance plant resilience to both abiotic and biotic stresses.

Fast, accurate, and non-destructive analytical techniques based on hyperspectral imaging (hsi) represent effective tools for food quality evaluation. A visible change in the appearance of a fresh product often negatively impacts the perceived quality from a consumer’s point of view.

All about “Australian grapevine stories”

This study aims to evaluate the interest of using an under-vine cover crop as a sustainable management tool replacing herbicides or tillage to control weeds, evaluating its effects on yield and berry parameters in a semi-arid climate. 
The performance of Trifolium fragiferum as an under-vine cover crop was evaluated in 2018 and 2019 in a Merlot vineyard in

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.