Monitoring vineyard canopy structure by aerial and ground-based RGB and multispectral imagery analysis

The purpose of this research study is to consider new solutions for distillate ageing, in alternative to conventional oak chips or barrels in particular sliced wood and peeled wood were compared to oak cubes, normally employed during both wine and distillate ageing.

The winegrowers of the intercommunal cooperative cellar of Saint Hilaire d’Ozilhan have been practicing terroir selection for ten years. Five years ago, after having equipped themselves with an efficient commercial structure, and anxious to improve knowledge of their terroirs and to better control quantitatively and qualitatively the range of typicality that they can develop, they asked the Syndicate Général des Vignerons Réunis des Côtes du Rhône and the Institut Coopératif du Vin to help them set up an approach to better judge the behavior of the Grenache and Syrah grape varieties in the different terroirs, then to enhance this work through the improving product quality.

Industry and regulatory agencies have developed regulations to ensure authenticity and compliance with wine composition limits. However, this can be truncated by the absence of simple and robust analytical methodologies, uninfluenced by the environment, different oenological techniques and cultural practices. Genetic fingerprinting is the most powerful tool for unequivocal varietal identification; it is not affected by the environment or agronomic practices; however, its usefulness in musts and wines has been controversial and there is currently no routine certification of varietal origin based on DNA analysis.

Grapevines are grown mainly as grafts worldwide, but the rootstocks most commonly used were selected between the late 19th and early 20th centuries and are based on reduced genetic diversity[1]. In the context of climate change, it is indeed urgent to diversify the range of rootstocks with genotypes much more adapted to drier environments, than the existing ones[2]. The aim of this study was to evaluate the potential of new genetic resources for grapevine rootstock breeding programs. For this purpose, 12 American and Asian wild Vitis species (3 to 5 accessions per species = 50 accessions) were evaluated for their rooting ability and drought response.

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.