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It is crucial for winegrowers to make informed decisions about the optimum time to harvest the grapes to ensure the production of premium wines. Global warming contributes to decreasing acidity and increasing sugar levels in grapes, resulting in bland wines with high contents of alcohol. Predicting quality in viticulture is thus pivotal. To assess the average ripeness, typically a sample of one hundred berries representative for the entire vineyard is collected. However, this process, along with the subsequent detailed must analysis, is time consuming and expensive. This study focusses on predicting essential quality parameters like sugar and acid content in Vitis vinifera (L.) varieties ‘Chardonnay’, ‘Riesling’, ‘Dornfelder’, and ‘Pinot Noir’. A small near-infrared spectrometer was used measuring non-destructively in the wavelength range from 1 100 nm to 1 350 nm while the reference contents were measured using high-performance liquid chromatography. Chemometric models were developed employing partial least squares regression and using spectra of all four grapevine varieties, spectra gained from berries of the same colour, or from the individual varieties. The models exhibited high accuracy in predicting main quality-determining parameters in independent test sets. On average, the model regression coefficients exceeded 93% for the sugars fructose and glucose, 86% for malic acid, and 73% for tartaric acid. Using these models, prediction accuracies revealed the ability to forecast individual sugar contents within an range of ± 6.97 g/L to ± 10.08 g/L, and malic acid within ± 2.01 g/L to ± 3.69 g/L. This approach indicates the potential to develop robust models by incorporating spectra from diverse grape varieties and berries of different colours. Such insight is crucial for the potential widespread adoption of a handheld near-infrared sensor, possibly integrated into devices used in everyday life, like smartphones. A server-side and cloud-based solution for pre-processing and modelling could thus avoid pitfalls of using near-infrared sensors on unknown varieties and in diverse wine-producing regions.

With the ongoing cost decrease of genotyping and sequencing technologies, accurate and fast phenotyping remains the bottleneck in the utilizing of plant genetic resources for breeding and breeding research. Although cost-efficient high-throughput phenotyping platforms are emerging for specific traits and/or species, manual phenotyping is still widely used and is a time- and money-consuming step. Approaches that improve data recording, processing or handling are pivotal steps towards the efficient use of genetic resources and are demanded by the research community. Therefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses. It is a versatile tool that offers the possibility to fully customize the descriptors/scales for any possible scenario, also in accordance with international information standards such as MIAPPE (Minimum Information About a Plant Phenotyping Experiment) and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Furthermore, PhenoApp enables the use of pre-integrated ready-to-use BBCH (Biologische Bundesanstalt für Land- und Forstwirtschaft, Bundessortenamt und CHemische Industrie) scales for apple, cereals, grapevine, maize, potato, rapeseed and rice. Additional BBCH scales can easily be added. The simple and adaptable structure of input and output files enables an easy data handling by either spreadsheet software or even the integration in the workflow of laboratory information management systems (LIMS). PhenoApp is therefore a decisive contribution to increase efficiency of digital data acquisition in genebank management but also contributes to breeding and breeding research by accelerating the labour intensive and time-consuming acquisition of phenotyping data.

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.

Botrytis bunch rot is one of the economically most important fungal diseases in viticulture (aside from powdery mildew and downy mildew). So far, no active defense mechanisms and resistance loci against the necrotrophic pathogen are known. Since long, breeders are mostly selecting phenotypically for loose grape bunches, which is recently the most evident trait to decrease the infection risk of Botrytis bunch rot. This study focused on plant phenomics of multiple traits by applying fast sensor technologies to measure berry impedance (ZREL), berry texture, and 3D bunch architecture. As references, microscopic determined cuticle thickness (MSCT) and infestation of grapes with Botrytis bunch rot were used. ZREL hereby is correlated to grape bunch density OIV204 (r = −0.6), cuticle thickness of berries (r = 0.61), mean berry diameter (r = −0.63), and Botrytis bunch rot (r = −0.7). However, no correlation between ZREL and berry maturity or berry texture was observed. In comparison to the category of traditional varieties (mostly susceptible), elite breeding lines show an impressive increased ZREL value (+317) and a 1-μm thicker berry cuticle. Quantitative trait loci (QTLs) on LGs 2, 6, 11, 15, and 16 were identified for ZREL and berry texture explaining a phenotypic variance of between 3 and 10.9%. These QTLs providing a starting point for the development of molecular markers. Modeling of ZREL and berry texture to predict Botrytis bunch rot resilience revealed McFadden R2 = 0.99. Taken together, this study shows that in addition to loose grape bunch architecture, berry diameter, ZREL, and berry texture values are probably additional parameters that could be used to identify and select Botrytis-resilient wine grape varieties. Furthermore, grapevine breeding will benefit from these reliable methodologies permitting high-throughput screening for additional resilience traits of mechanical and physical barriers to Botrytis bunch rot. The findings might also be applicable to table grapes and other fruit crops like tomato or blueberry.

In viticulture, winemakers and the industry demand grape bunches that have a reduced degree of bunch compactness. The major aspect is that a loose bunch compactness reduces the risk of severe Botrytis bunch-rot infections. Grapevine breeders focus hereby on several bunch-architecture-related traits. For specific breeding approaches and breeding-research-related topics, such as Quantitative Trait Locus (QTL) analysis or molecular marker development, the exact and objective phenotyping of such traits is mandatory. In this study, a precise and high-throughput 3D phenotyping pipeline was applied to screen 1514 genotypes from three mapping populations with different genetic backgrounds to investigate its applicability for QTL mapping approaches. In the first step, the phenotypic data of one population containing 150 genotypes were collected and analyzed with the 3D phenotyping pipeline. Additionally, corresponding reference data were obtained. Phenotypic values and results of a QTL analysis were compared with each other. Strongly positive correlations up to r = 0.93 between 3D and reference measurements could be detected for several traits. The ten-times-faster 3D phenotyping pipeline revealed 20, and the reference phenotyping methods revealed 22 QTLs. Eighteen of these QTLs were consistent between both procedures. In the next step, screening was extended to four different mapping populations across several seasons. In total, up to 1500 genotypes were screened during one season (>5000 grape bunches in total). The data analysis revealed significant differences across years and populations. Three bunch-architecture traits, including total berry volume, bunch width, and berry diameter, explained the highest amount of variability in the phenotypic data. A QTL analysis was performed on the phenotypic data of the involved populations to identify comparative genetic loci for bunch-architecture traits. Between 20 and 26 stable and reproducible QTLs for the investigated populations were detected. A common QTL for berry diameter could be identified for all populations. Our results strongly conclude that this locus is co-located on chromosome 17 when mapped to the grapevine reference genome. The results show that the implementation of the 3D phenotyping platform allows for precise and extended screenings of different, genetic diverse mapping populations and thus opens up the possibility to uncover the genomic architecture of this highly complex quantitative grapevine trait.

The aim of this study was to determine the effects of defoliation performed in the Babica red grape variety on the volatile compounds in produced wine. Three treatments were performed during 2017 and 2018: the removal of six leaves before flowering (FL) and at the end of veraison (VER), as well as control (C). Volatile compounds were analyzed using a gas chromatograph coupled to a mass spectrophotometric detector. Results were statistically evaluated by analysis of variance (ANOVA at the p = 0.05 level) and principal component analysis (PCA). Defoliation treatments were affected by the concentration of several compounds, but only in one year. The VER2017 treatment significantly increased the concentration of three aliphatic esters up to 8 C atoms and octanoic acid ethyl ester. The FL2017 treatment increased the concentration of three aliphatic alcohols. The FL2018 treatment has significantly enhanced the concentration ethyl cinnamate but decreased the concentrations of eugenol and dihydro-2-methyl-3(2H)-thiophenone. Both defoliation treatments reduced the concentration of γ-decanolactone in 2017. Aldehydes, monoterpenoles, and monoterpenes remained unaffected by the defoliation treatments. Vintage was found to be the largest source of variability for most volatile compounds under investigation, which was confirmed by PCA. The effect of defoliation in the mild-Mediterranean climate was found to mostly depend on seasonal weather conditions.

With the ongoing cost decrease of genotyping and sequencing technologies, accurate and fast phenotyping remains the bottleneck in the utilizing of plant genetic resources for breeding and breeding research. Although cost-efficient high-throughput phenotyping platforms are emerging for specific traits and/or species, manual phenotyping is still widely used and is a time- and money-consuming step. Approaches that improve data recording, processing or handling are pivotal steps towards the efficient use of genetic resources and are demanded by the research community. Therefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses. It is a versatile tool that offers the possibility to fully customize the descriptors/scales for any possible scenario, also in accordance with international information standards such as MIAPPE (Minimum Information About a Plant Phenotyping Experiment) and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Furthermore, PhenoApp enables the use of pre-integrated ready-to-use BBCH (Biologische Bundesanstalt für Land- und Forstwirtschaft, Bundessortenamt und CHemische Industrie) scales for apple, cereals, grapevine, maize, potato, rapeseed and rice. Additional BBCH scales can easily be added. The simple and adaptable structure of input and output files enables an easy data handling by either spreadsheet software or even the integration in the workflow of laboratory information management systems (LIMS). PhenoApp is therefore a decisive contribution to increase efficiency of digital data acquisition in genebank management but also contributes to breeding and breeding research by accelerating the labour intensive and time-consuming acquisition of phenotyping data.

With the ongoing cost decrease of genotyping and sequencing technologies, accurate and fast phenotyping remains the bottleneck in the utilizing of plant genetic resources for breeding and breeding research. Although cost-efficient high-throughput phenotyping platforms are emerging for specific traits and/or species, manual phenotyping is still widely used and is a time- and money-consuming step. Approaches that improve data recording, processing or handling are pivotal steps towards the efficient use of genetic resources and are demanded by the research community. Therefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses. It is a versatile tool that offers the possibility to fully customize the descriptors/scales for any possible scenario, also in accordance with international information standards such as MIAPPE (Minimum Information About a Plant Phenotyping Experiment) and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Furthermore, PhenoApp enables the use of pre-integrated ready-to-use BBCH (Biologische Bundesanstalt für Land- und Forstwirtschaft, Bundessortenamt und CHemische Industrie) scales for apple, cereals, grapevine, maize, potato, rapeseed and rice. Additional BBCH scales can easily be added. The simple and adaptable structure of input and output files enables an easy data handling by either spreadsheet software or even the integration in the workflow of laboratory information management systems (LIMS). PhenoApp is therefore a decisive contribution to increase efficiency of digital data acquisition in genebank management but also contributes to breeding and breeding research by accelerating the labour intensive and time-consuming acquisition of phenotyping data.