Implementation of hyperspectral image analysis for evaluating table grape quality on bunch and berry level

The evaluation of new grapevine genotypes regarding their potential to produce high quality wines is the time limiting factor in the process of grapevine breeding. Hence, the development of quality-related markers useable in marker-assisted selection (MAS) as well as in prediction models for this bottleneck trait will tremendously enhance breeding efficiency. In extensive studies a training set of a segregating white wine F1 population (150 F1 genotypes = POP150; `Calardis Musqué´ x `Villard Blanc´) was deeply phenotyped and genotyped for model development and QTL analysis.

Grape postharvest dehydration is a widely used technique for the special wines production, where genetic features, ripeness degree and environmental factors strongly influence the metabolic processes [1].

Selected strains of non-Saccharomyces yeasts showed a positive effect on sensory characteristics and aromatic complexity of wine. A sequential microbial culture of non-Saccharomyces and S. cerevisiae species is usually inoculated due to poorer fermentability of non-Saccharomyces species. The aim of the study was to investigate the role of non-Saccharomyces yeasts in the production of white wines. We evaluated how individual combinations of sequential inoculations of non-Saccharomyces and S. cerevisiae species affect the aromatic compounds (volatile thiols and esters) and sensory characteristics of the wines.

There is currently a real need to improve and speed-up phenotyping in experimental set-ups to increase the number of modalities studied. Accurate information acquisition on plant status with high-throughput capacity is the main appeal of on-board systems.
A proximal sensing camera for a proxy of winter pruning weight was tested. We estimated the shoot volume of the vine by image analysis using algorithms that integrate the local shoot section area estimate along the shoot skeleton obtained by a morphological distance transform.
The study was carried out on the GreffAdapt experimental vineyard in Guyot simple training and a canopy management using vertical trellising. The planting density is 6250 vines/ha with a row spacing of 1.6×1m. Five scions grafted onto 55 rootstocks are present and the combination rootstock×scion is different every five plants.

Performing accurate vineyard yield estimation is of upmost importance as it provides important benefits to the whole vine and wine industry. Recently, image-analysis approaches have been explored to address this issue however this approach has as main challenge the bunch occlusion, mostly by vegetation but also by neighboring bunches. The present work aims at assessing the magnitude of bunch occlusion by neighboring bunches and to evaluate its dependency on a selection of vegetative and reproductive vine parameters assessed at fruiting zone. Forty vine segments (1 m) of two vineyard plots of the white cultivars ‘Alvarinho’ and ‘Arinto’ were assessed for vegetative and reproductive features at fruiting zone and imaged with a 2D camera.