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A multitude of diverse breeding goals need to be combined in a new cultivar, which always forces to compromise. The biggest challenge grapevine breeders face is the extraordinarily complex trait of wine quality, which is the all-pervasive and most debated characteristic. Since the 1920s, Germany runs continuous grapevine breeding programmes. This continuity was the key to success and lead to various new cultivars on the market, so called PIWIs. Initially, introduced pests and diseases such as phylloxera, powdery and downy mildew were the driving forces for breeding. However, preconceptions about the wine quality of new resistant selections impeded the market introduction. These preconceptions are still echoing today and may be the reason in large parts of the viticultural community for: (1) ignoring substantial breeding progress, and (2) sticking to successful markets of well-known varietal wines or blends (e.g. Chardonnay, Cabernet Sauvignon, Riesling). New is the need to improve viticulture´s sustainability and to adapt to changing environmental conditions. Climate change with its extreme weather will impose the need for a change in cultivars in many wine growing regions. Therefore, a paradigm shift is knocking on the door: new varieties (PIWIs) versus traditional varieties for climate adapted and sustainable viticulture. However, it will be slow process and viticulture is politically well advised to pave the way to variety innovation. In contrast to the widely available PIWIs, competitive cultivars created by means of new breeding technologies (NBT, e.g. through CRISPR/Cas) are still decades from introduction to the market.

Warm and moist weather conditions during berry ripening provoke Botrytis cinerea (B. cinerea) causing notable bunch rot on susceptible grapevines with the effect of reduced yield and wine quality. Resistance donors of genetic loci to increase B. cinerea resistance are widely unknown. Promising traits of resistance are represented by physical features like the thickness and permeability of the grape berry cuticle. Sensor-based phenotyping methods or genetic markers are rare for such traits. In the present study, the simple-to-handle I-sensor was developed. The sensor enables the fast and reliable measurement of electrical impedance of the grape berry cuticles and its epicuticular waxes (CW). Statistical experiments revealed highly significant correlations between relative impedance of CW and the resistance of grapevines to B. cinerea. Thus, the relative impedance Zrel of CW was identified as the most important phenotypic factor with regard to the prediction of grapevine resistance to B. cinerea. An ordinal logistic regression analysis revealed a R2McFadden of 0.37 and confirmed the application of Zrel of CW for the prediction of bunch infection and in this way as novel phenotyping trait. Applying the I-sensor, a preliminary QTL region was identified indicating that the novel phenotypic trait is as well a valuable tool for genetic analyses.

A population derived from a cross between grapevine breeding strain Gf.Ga-52-42 and cultivar ‘Solaris’ consisting of 265 F1-individuals was genetically mapped using SSR markers and screened for downy mildew resistance. Quantitative trait locus (QTL) analysis revealed two strong QTLs on linkage groups (LGs) 18 and 09. The locus on LG 18 was found to be identical with the previously described locus Rpv3 and is transmitted by Gf.Ga-52-42. ‘Solaris’ transmitted the resistance-related locus on LG 09 explaining up to 50% of the phenotypic variation in the population. This downy mildew resistance locus is named Rpv10 for resistance to Plasmopara viticola . Rpv10 was initially introgressed from Vitis amurensis , a wild species of the Asian Vitis gene pool. The one-LOD supported confidence interval of the QTL spans a section of 2.1 centi Morgan (cM) corresponding to 314 kb in the reference genome PN40024 (12x). Eight resistance gene analogues (RGAs) of the NBS–LRR type and additional resistance-linked genes are located in this region of PN40024. The F1 sub-population which contains the Rpv3 as well as the Rpv10 locus showed a significantly higher degree of resistance, indicating additive effects by pyramiding of resistance loci. Possibilities for using the resistance locus Rpv10 in a grapevine breeding programme are discussed. Furthermore, the marker data revealed ‘Severnyi’ × ‘Muscat Ottonel’ as the true parentage for the male parent of ‘Solaris’.

The hairiness of the leaves is an essential morphological feature within the genus Vitis that can serve as a physical barrier. A high leaf hair density present on the abaxial surface of the grapevine leaves influences their wettability by repelling forces, thus preventing pathogen attack such as downy mildew and anthracnose. Moreover, leaf hairs as a favorable habitat may considerably affect the abundance of biological control agents. The unavailability of accurate and efficient objective tools for quantifying leaf hair density makes the study intricate and challenging. Therefore, a validated high-throughput phenotyping tool was developed and established in order to detect and quantify leaf hair using images of single grapevine leaf discs and convolution neural networks (CNN). We trained modified ResNet CNNs with a minimalistic number of images to efficiently classify the area covered by leaf hairs. This approach achieved an overall model prediction accuracy of 95.41%. As final validation, 10,120 input images from a segregating F1 biparental population were used to evaluate the algorithm performance. ResNet CNN-based phenotypic results compared to ground truth data received by two experts revealed a strong correlation with R values of 0.98 and 0.92 and root-mean-square error values of 8.20% and 14.18%, indicating that the model performance is consistent with expert evaluations and outperforms the traditional manual rating. Additional validation between expert vs. non-expert on six varieties showed that non-experts contributed to over- and underestimation of the trait, with an absolute error of 0% to 30% and -5% to -60%, respectively. Furthermore, a panel of 16 novice evaluators produced significant bias on set of varieties. Our results provide clear evidence of the need for an objective and accurate tool to quantify leaf hairiness.

In viticulture, phenotypic data are traditionally collected directly in the field via visual and manual means by an experienced person. This approach is time consuming, subjective and prone to human errors. In recent years, research therefore has focused strongly on developing automated and non-invasive sensor-based methods to increase data acquisition speed, enhance measurement accuracy and objectivity and to reduce labor costs. While many 2D methods based on image processing have been proposed for field phenotyping, only a few 3D solutions are found in the literature. A track-driven vehicle consisting of a camera system, a real-time-kinematic GPS system for positioning, as well as hardware for vehicle control, image storage and acquisition is used to visually capture a whole vine row canopy with georeferenced RGB images. In the first post-processing step, these images were used within a multi-view-stereo software to reconstruct a textured 3D point cloud of the whole grapevine row. A classification algorithm is then used in the second step to automatically classify the raw point cloud data into the semantic plant components, grape bunches and canopy. In the third step, phenotypic data for the semantic objects is gathered using the classification results obtaining the quantity of grape bunches, berries and the berry diameter.

Wine growers prefer cultivars with looser bunch architecture because of the decreased risk for bunch rot. As a consequence, grapevine breeders have to select seedlings and new cultivars with regard to appropriate bunch traits. Bunch architecture is a mosaic of different single traits which makes phenotyping labor-intensive and time-consuming. In the present study, a fast and high-precision phenotyping pipeline was developed. The optical sensor Artec Spider 3D scanner (Artec 3D, L-1466, Luxembourg) was used to generate dense 3D point clouds of grapevine bunches under lab conditions and an automated analysis software called 3D-Bunch-Tool was developed to extract different single 3D bunch traits, i.e., the number of berries, berry diameter, single berry volume, total volume of berries, convex hull volume of grapes, bunch width and bunch length. The method was validated on whole bunches of different grapevine cultivars and phenotypic variable breeding material. Reliable phenotypic data were obtained which show high significant correlations (up to r2 = 0.95 for berry number) compared to ground truth data. Moreover, it was shown that the Artec Spider can be used directly in the field where achieved data show comparable precision with regard to the lab application. This non-invasive and non-contact field application facilitates the first high-precision phenotyping pipeline based on 3D bunch traits in large plant sets.

The present study is the first in-depth research evaluating the genetic diversity and potential resistance of Armenian wild grapes utilizing DNA-based markers to understand the genetic signature of this unexplored germplasm. In the proposed research, five geographical regions with known viticultural history were explored. A total of 148 unique wild genotypes were collected and included in the study with 48 wild individuals previously collected as seed. A total of 24 nSSR markers were utilized to establish a fingerprint database to infer information on the population genetic diversity and structure. Three nSSR markers linked to the Ren1 locus were analyzed to identify potential resistance against powdery mildew. According to molecular fingerprinting data, the Armenian V. sylvestris gene pool conserves a high genetic diversity, displaying 292 different alleles with 12.167 allele per loci. The clustering analyses and diversity parameters supported eight genetic groups with 5.6% admixed proportion. The study of genetic polymorphism at the Ren1 locus revealed that 28 wild genotypes carried three R-alleles and 34 wild genotypes carried two R-alleles associated with PM resistance among analyzed 107 wild individuals. This gene pool richness represents an immense reservoir of under-explored genetic diversity and breeding potential. Therefore, continued survey and research efforts are crucial for the conservation, sustainable management, and utilization of Armenian wild grape resources in the face of emerging challenges in viticulture.

Grapevine yellows (GY) are serious phytoplasma-caused diseases affecting viticultural areas worldwide. At present, two principal agents of GY are known to infest grapevines in Germany: Bois noir (BN) and Palatinate grapevine yellows (PGY). Disease management is mostly based on prophylactic measures as there are no curative in-field treatments available. In this context, sensor-based disease detection could be a useful tool for winegrowers. Therefore, hyperspectral imaging (400–2500 nm) was applied to identify phytoplasma-infected greenhouse plants and shoots collected in the field. Disease detection models (Radial-Basis Function Network) have successfully been developed for greenhouse plants of two white grapevine varieties infected with BN and PGY. Differentiation of symptomatic and healthy plants was possible reaching satisfying classification accuracies of up to 96%. However, identification of BN-infected but symptomless vines was difficult and needs further investigation. Regarding shoots collected in the field from different red and white varieties, correct classifications of up to 100% could be reached using a Multi-Layer Perceptron Network for analysis. Thus, hyperspectral imaging seems to be a promising approach for the detection of different GY. Moreover, the 10 most important wavelengths were identified for each disease detection approach, many of which could be found between 400 and 700 nm and in the short-wave infrared region (1585, 2135, and 2300 nm). These wavelengths could be used further to develop multispectral systems.

The Eurasian grapevine ( Vitis vinifera ) has long been important for wine production as well as being a food source. Despite being clonally propagated, modern cultivars exhibit great morphological and genetic diversity, with thousands of varieties described in historic and contemporaneous records. Through historical accounts, some varieties can be traced to the Middle Ages, but the genetic relationships between ancient and modern vines remain unknown. We present target-enriched genome-wide sequencing data from 28 archaeological grape seeds dating to the Iron Age, Roman era and medieval period. When compared with domesticated and wild accessions, we found that the archaeological samples were closely related to western European cultivars used for winemaking today. We identified seeds with identical genetic signatures present at different Roman sites, as well as seeds sharing parent–offspring relationships with varieties grown today. Furthermore, we discovered that one seed dated to ~1100 ce was a genetic match to ‘Savagnin Blanc’, providing evidence for 900 years of uninterrupted vegetative propagation. Thousands of varieties of wine grapes have been recorded and described in historical accounts, some going back as far as the Middle Ages, but genetic relationships between ancient and modern varieties were unknown. Genomic sequencing of 28 seeds, dating back as far as the Iron Age, finds close relationships to today’s cultivars, including a genetic match to Savagnin Blanc from 1100 ce .

An automatic determination of grape must ingredients during the harvesting process would support cellar logistics and enables an early termination of the harvest if quality parameters are not met. One of the most important quality-determining characteristics of grape must is its sugar and acid content. Among others, the sugars in particular determine the quality of the must and wine. Chiefly in wine cooperatives, in which a third of all German winegrowers are organized, these quality characteristics serve as the basis for payment. They are acquired upon delivery at the cellar of the cooperative or the winery and result in the acceptance or rejection of grapes and must. The whole process is very time-consuming and expensive, and sometimes grapes that do not meet the quality requirements for sweetness, acidity, or healthiness are destroyed or not used at all, which leads to economic loss. Near-infrared spectroscopy is now a widely used technique to detect a wide variety of ingredients in biological samples. In this study, a miniaturized semi-automated prototype apparatus with a near-infrared sensor and a flow cell was used to acquire spectra (1100 nm to 1350 nm) of grape must at defined temperatures. Data of must samples from four different red and white Vitis vinifera (L.) varieties were recorded throughout the whole growing season of 2021 in Rhineland Palatinate, Germany. Each sample consisted of 100 randomly sampled berries from the entire vineyard. The contents of the main sugars (glucose and fructose) and acids (malic acid and tartaric acid) were determined with high-performance liquid chromatography. Chemometric methods, using partial least-square regression and leave-one-out cross-validation, provided good estimates of both sugars (RMSEP = 6.06 g/L, R2 = 89.26%), as well as malic acid (RMSEP = 1.22 g/L, R2 = 91.10%). The coefficient of determination (R2) was comparable for glucose and fructose with 89.45% compared to 89.08%, respectively. Although tartaric acid was predictable for only two of the four varieties using near-infrared spectroscopy, calibration and validation for malic acid were accurate for all varieties in an equal extent like the sugars. These high prediction accuracies for the main quality determining grape must ingredients using this miniaturized prototype apparatus might enable an installation on a grape harvester in the future.