Hyperspectral imaging and machine learning for monitoring grapevine physiology

New and developing technologies, that provide sensors and the software systems for using and interpreting them, are becoming pervasive through our lives and society. From smart phones to cars to farm machinery, all contain a range of sensors that are monitored automatically with intelligent software, providing us with the information we need, when we need it. This technological revolution has the potential to monitor all aspects of vineyard activity, assisting growers to make the management choices they need to achieve the outcomes they want. For example, a future vineyard may possess automated imaging that generates a three dimensional model of the vine canopy, highlighting differences from the desired structure and how to use canopy management to improve fruit composition, or generates maps with yield estimates and measurements of berry composition throughout the growing season.

Crimson Seedless’ (Vitis vinifera L.) is a late-ripening, red seedless table grape cultivar with inadequate anthocyanin accumulation and less than ideal berry size issues

Advanced knowledge on grapevine row orientation is required to improve establishment, management and outcomes of vineyards on terroirs with different environmental conditions (climate, soil, topography) and in view of a future change to more extreme climatic conditions. The purpose of this study was to determine the combined effect of row orientation, plant water status and ripeness level on the physiological and viticultural reaction of Shiraz/101-14 Mgt.

Three-dimensional functional-structural root architecture models, which decompose the root system architecture (RSA) into elementary developmental processes such as root emission, axial growth, branching patterns and tropism have become useful tools for (i) reconstructing in silico the spatial and temporal dynamics of root systems in a soil volume, (ii) analyzing their genotypic diversity and plasticity to the environment, and (iii) overcoming the bottleneck associated with their visualization and measurement in situ. Here, we present an original work on RSA phenotyping and modelling in grapevine. First, we developed 2D image-based analysis pipelines to quantify morphological and architectural traits in young grafts. Second, we parametrized and validated the 3D root model Archisimple on two rootstock genotypes (RGM, 1103P) grafted with V. vinifera Cabernet-Sauvignon and grown in different controlled conditions (rhizotrons, pots, tubes).

In Alentejo, south of Portugal there is a traditional way of fermenting wines in clay vessels, known as “Vinhos de Talha”. Clay vessels were traditionally impermeabilized using pine pitch, creating a barrier between the fermenting must and the clay. Due to this unusual production technology that uses of clay vessels, instead of inox or wood vessels, “Vinhos de Talha” present unique characteristics increasingly appreciated by national and international consumers when compared with wine obtained by the said traditional methods of winemaking. Although the positive consumers feedback, there is little literature about the physical-chemical characteristics of these wines (Martins et al, 2018; Cabrita et al, 2018). This work aims to characterize the volatile composition of white wines produced in clay vessels with different coatings and to contribute to the knowledge and preservation of these wines that are a unique cultural heritage. Wine samples were produced during 2019 vintage from white grapes, using the traditional technology associated to these wines.