A Non-Invasive Method Based on Computer Vision for Grapevine Cluster Compactness Assessment Using a Mobile Sensing Platform under Field Conditions.

Grapevine cluster compactness affects grape composition, fungal disease incidence, and wine quality. Thus far, cluster compactness assessment has been based on visual inspection performed by trained evaluators with very scarce application in the wine industry. The goal of this work was to develop a new, non-invasive method based on the combination of computer vision and machine learning technology for cluster compactness assessment under field conditions from on-the-go red, green, blue (RGB) image acquisition. A mobile sensing platform was used to automatically capture RGB images of grapevine canopies and fruiting zones at night using artificial illumination. Likewise, a set of 195 clusters of four red grapevine varieties of three commercial vineyards were photographed during several years one week prior to harvest. After image acquisition, cluster compactness was evaluated by a group of 15 experts in the laboratory following the International Organization of Vine and Wine (OIV) 204 standard as a reference method. The developed algorithm comprises several steps, including an initial, semi-supervised image segmentation, followed by automated cluster detection and automated compactness estimation using a Gaussian process regression model. Calibration (95 clusters were used as a training set and 100 clusters as the test set) and leave-one-out cross-validation models (LOOCV; performed on the whole 195 clusters set) were elaborated. For these, determination coefficient (R2) of 0.68 and a root mean squared error (RMSE) of 0.96 were obtained on the test set between the image-based compactness estimated values and the average of the evaluators' ratings (in the range from 1-9). Additionally, the leave-one-out cross-validation yielded a R2 of 0.70 and an RMSE of 1.11. The results show that the newly developed computer vision based method could be commercially applied by the wine industry for efficient cluster compactness estimation from RGB on-the-go image acquisition platforms in commercial vineyards.

On-The-Go VIS + SW - NIR Spectroscopy as a Reliable Monitoring Tool for Grape Composition within the Vineyard.

Visible-Short Wave Near Infrared (VIS + SW - NIR) spectroscopy is a real alternative to break down the next barrier in precision viticulture allowing a reliable monitoring of grape composition within the vineyard to facilitate the decision-making process dealing with grape quality sorting and harvest scheduling, for example. On-the-go spectral measurements of grape clusters were acquired in the field using a VIS + SW - NIR spectrometer, operating in the 570-990 nm spectral range, from a motorized platform moving at 5 km/h. Spectral measurements were acquired along four dates during grape ripening in 2017 on the east side of the canopy, which had been partially defoliated at cluster closure. Over the whole measuring season, a total of 144 experimental blocks were monitored, sampled and their fruit analyzed for total soluble solids (TSS), anthocyanin and total polyphenols concentrations using standard, wet chemistry reference methods. Partial Least Squares (PLS) regression was used as the algorithm for training the grape composition parameters' prediction models. The best cross-validation and external validation (prediction) models yielded determination coefficients of cross-validation (R2cv) and prediction (R2P) of 0.92 and 0.95 for TSS, R2cv = 0.75, and R2p = 0.79 for anthocyanins, and R2cv = 0.42 and R2p = 0.43 for total polyphenols. The vineyard variability maps generated for the different dates using this technology illustrate the capability to monitor the spatiotemporal dynamics and distribution of total soluble solids, anthocyanins and total polyphenols along grape ripening in a commercial vineyard.

Assessment of amino acids and total soluble solids in intact grape berries using contactless Vis and NIR spectroscopy during ripening.

The amino acid concentration assessment along grape ripening would provide valuable information regarding harvest scheduling, wine aroma potential and must nitrogen supplement addition. In this work the use of Visible (Vis) and near-infrared (NIR) spectroscopy to estimate the grape amino acid content along maturation on intact berries was investigated. Spectral data on two ranges (570-1000 and 1100-2100 nm) were acquired contactless from intact Grenache berries. A total of 22 free amino acids in 128 grape clusters were quantified by HPLC. Partial least squares was used to build calibration, cross validation and prediction models. The best performances (R2P ~ 0.60) were found for asparagine (SEP: 0.45 mg N/l), tyrosine (SEP: 0.33 mg N/l) and proline (SEP: 17.5 mg N/l) in the 570-1000 nm range, and for lysine (SEP: 0.44 mg N/l), tyrosine (SEP: 0.26 mg N/l), and proline (SEP: 15.54 mg N/l) in the 1100-2100 nm range. Remarkable models (R2P~0.90, SEP~1.60 ºBrix, and RPD~3.79) were built for total soluble solids in both spectral ranges. Contactless, non-destructive spectroscopy could be an alternative to provide information about grape amino acids composition.

On-The-Go Hyperspectral Imaging Under Field Conditions and Machine Learning for the Classification of Grapevine Varieties.

Grapevine varietal classification is an important plant phenotyping issue for grape growing and wine industry. This task has been achieved from destructive techniques like classic ampelography and DNA analysis under laboratory conditions. This work displays a new approach for the classification of a high number of grapevine (Vitis vinifera L.) varieties under field conditions using on-the-go hyperspectral imaging and different machine learning algorithms. On-the-go imaging was performed under natural illumination using a hyperspectral camera mounted on an all-terrain vehicle at 5 km/h. Spectra were acquired over two different leaf phenological stages on the canopy of 30 different varieties on a commercial vineyard located in La Rioja, Spain. A total of 1,200 spectral samples were generated. Support vector machines (SVM) and artificial neural networks (multilayer perceptrons, MLP) were used for the development of a large number of models, testing different algorithm parameters and spectral pre-processing techniques. Both classifiers yielded notable performance values and were able to train models with recall F1 scores and area under the receiver operating characteristic curve marks up to 0.99 for 5-fold cross validation. Statistical analyses supported that the best SVM kernel was linear and the best activation function for MLP was the hyperbolic tangent function. The prediction performance for individual varieties of MLP ranged from 0.94 to 0.99, displaying low levels of variability. In the case of SVM, slightly higher differences were obtained, ranging from 0.83 to 0.97 for individual varieties. These results support the possibility of deploying an on-the-go hyperspectral imaging system in the field capable of successfully classifying leaves from different grapevine varieties. This technology could thus be considered as a new useful non-destructive tool for plant phenotyping under field conditions.

Effects of soil erosion on agro-ecosystem services and soil functions: A multidisciplinary study in nineteen organically farmed European and Turkish vineyards.

This multidisciplinary research work evaluated the effects of soil erosion on grape yield and quality and on different soil functions, namely water and nutrient supply, carbon sequestration, organic matter recycling, and soil biodiversity, with the aim to understand the causes of soil malfunctioning and work out a proper strategy of soil remediation. Degraded areas in nineteen organically farmed European and Turkish vineyards resulted in producing significantly lower amounts of grapes and excessive concentrations of sugar. Plants suffered from decreased water nutrition, due to shallower rooting depth, compaction, and reduced available water capacity, lower chemical fertility, as total nitrogen and cation exchange capacity, and higher concentration of carbonates. Carbon storage and organic matter recycling were also depressed. The general trend of soil enzyme activity mainly followed organic matter stock. Specific enzymatic activities suggested that in degraded soils, alongside a general slowdown in organic matter cycling, there was a greater reduction in decomposition capacity of the most recalcitrant forms. The abundance of Acari Oribatida and Collembola resulted the most sensitive indicator of soil degradation among the considered microarthropods. No clear difference in overall microbial richness and evenness were observed. All indices were relatively high and indicative of rich occurrence of many and rare microbial species. Dice cluster analyses indicated slight qualitative differences in Eubacterial and fungal community compositions in rhizosphere soil and roots in degraded soils. This multidisciplinary study indicates that the loss of soil fertility caused by excessive earth movement before planting, or accelerated erosion, mainly affects water nutrition and chemical fertility. Biological soil fertility is also reduced, in particular the ability of biota to decompose organic matter, while biodiversity is less affected, probably because of the organic management. Therefore, the restoration of the eroded soils requires site-specific and intensive treatments, including accurately chosen organic matrices for fertilization, privileging the most easily decomposable. Restoring soil fertility in depth, however, remain an open question, which needs further investigation.

Geographical and Cultivar Features Differentiate Grape Microbiota in Northern Italy and Spain Vineyards.

Recent studies have highlighted the role of the grapevine microbiome in addressing a wide panel of features, ranging from the signature of field origin to wine quality. Although the influence of cultivar and vineyard environmental conditions in shaping the grape microbiome have already been ascertained, several aspects related to this topic, deserve to be further investigated. In this study, we selected three international diffused grapevine cultivars (Cabernet Sauvignon, Syrah, and Sauvignon Blanc) at three germplasm collections characterized by different climatic conditions [Northern Italy (NI), Italian Alps (AI), and Northern Spain (NS)]. The soil and grape microbiome was characterized by 16s rRNA High Throughput Sequencing (HTS), and the obtained results showed that all grape samples shared some bacterial taxa, regardless of sampling locality (e.g., Bacillus, Methylobacterium, Sphingomonas, and other genera belonging to Alphaproteobacteria, Gammaproteobacteria, and Actinobacteria). However, some Operational Taxonomic Units (OTUs) could act as geographical signatures and in some cases as cultivar fingerprint. Concerning the origin of the grape microbiome, our study confirms that vineyard soil represents a primary reservoir for grape associated bacteria with almost 60% of genera shared between the soil and grape. At each locality, grapevine cultivars shared a core of bacterial genera belonging to the vineyard soil, as well as from other local biodiversity elements such as arthropods inhabiting or foraging in the vineyard. Finally, a machine learning analysis showed that it was possible to predict the geographical origin and cultivar of grape starting from its microbiome composition with a high accuracy (9 cases out of 12 tested samples). Overall, these findings open new perspectives for the development of more comprehensive and integrated research activities to test which environmental variables have an effective role in shaping the microbiome composition and dynamics of cultivated species over time and space.

Development and Validation of a New Methodology to Assess the Vineyard Water Status by On-the-Go Near Infrared Spectroscopy.

Assessing water status and optimizing irrigation is of utmost importance in most winegrowing countries, as the grapevine vegetative growth, yield, and grape quality can be impaired under certain water stress situations. Conventional plant-based methods for water status monitoring are either destructive or time and labor demanding, therefore unsuited to detect the spatial variation of moisten content within a vineyard plot. In this context, this work aims at the development and comprehensive validation of a novel, non-destructive methodology to assess the vineyard water status distribution using on-the-go, contactless, near infrared (NIR) spectroscopy. Likewise, plant water status prediction models were built and intensely validated using the stem water potential (ψs) as gold standard. Predictive models were developed making use of a vast number of measurements, acquired on 15 dates with diverse environmental conditions, at two different spatial scales, on both sides of vertical shoot positioned canopies, over two consecutive seasons. Different cross-validation strategies were also tested and compared. Predictive models built from east-acquired spectra yielded the best performance indicators in both seasons, with determination coefficient of prediction ([Formula: see text]) ranging from 0.68 to 0.85, and sensitivity (expressed as prediction root mean square error) between 0.131 and 0.190 MPa, regardless the spatial scale. These predictive models were implemented to map the spatial variability of the vineyard water status at two different dates, and provided useful, practical information to help delineating specific irrigation schedules. The performance and the large amount of data that this on-the-go spectral solution provides, facilitates the exploitation of this non-destructive technology to monitor and map the vineyard water status variability with high spatial and temporal resolution, in the context of precision and sustainable viticulture.

Vineyard water status assessment using on-the-go thermal imaging and machine learning.

The high impact of irrigation in crop quality and yield in grapevine makes the development of plant water status monitoring systems an essential issue in the context of sustainable viticulture. This study presents an on-the-go approach for the estimation of vineyard water status using thermal imaging and machine learning. The experiments were conducted during seven different weeks from July to September in season 2016. A thermal camera was embedded on an all-terrain vehicle moving at 5 km/h to take on-the-go thermal images of the vineyard canopy at 1.2 m of distance and 1.0 m from the ground. The two sides of the canopy were measured for the development of side-specific and global models. Stem water potential was acquired and used as reference method. Additionally, reference temperatures Tdry and Twet were determined for the calculation of two thermal indices: the crop water stress index (CWSI) and the Jones index (Ig). Prediction models were built with and without considering the reference temperatures as input of the training algorithms. When using the reference temperatures, the best models casted determination coefficients R2 of 0.61 and 0.58 for cross validation and prediction (RMSE values of 0.190 MPa and 0.204 MPa), respectively. Nevertheless, when the reference temperatures were not considered in the training of the models, their performance statistics responded in the same way, returning R2 values up to 0.62 and 0.65 for cross validation and prediction (RMSE values of 0.190 MPa and 0.184 MPa), respectively. The outcomes provided by the machine learning algorithms support the use of thermal imaging for fast, reliable estimation of a vineyard water status, even suppressing the necessity of supervised acquisition of reference temperatures. The new developed on-the-go method can be very useful in the grape and wine industry for assessing and mapping vineyard water status.

Non-destructive assessment of grapevine water status in the field using a portable NIR spectrophotometer.

BACKGROUND: Until now, the majority of methods employed to assess grapevine water status have been destructive, time-intensive, costly and provide information of a limited number of samples, thus the ability of revealing within-field water status variability is reduced. The goal of this work was to evaluate the capability of non-invasive, portable near infrared (NIR) spectroscopy acquired in the field, to assess the grapevine water status in diverse varieties, grown under different environmental conditions, in a fast and reliable way. The research was conducted 2 weeks before harvest in 2012, in two commercial vineyards, planted with eight different varieties. Spectral measurements were acquired in the field on the adaxial and abaxial sides of 160 individual leaves (20 leaves per variety) using a commercially available handheld spectrophotometer (1600-2400 nm). RESULTS: Principal component analysis (PCA) and modified partial least squares (MPLS) were used to interpret the spectra and to develop reliable prediction models for stem water potential (Ψs ) (cross-validation correlation coefficient (rcv ) ranged from 0.77 to 0.93, and standard error of cross validation (SECV) ranged from 0.10 to 0.23), and leaf relative water content (RWC) (rcv ranged from 0.66 to 0.81, and SECV between 1.93 and 3.20). The performance differences between models built from abaxial and adaxial-acquired spectra is also discussed. CONCLUSIONS: The capability of non-invasive NIR spectroscopy to reliably assess the grapevine water status under field conditions was proved. This technique can be a suitable and promising tool to appraise within-field variability of plant water status, helpful to define optimised irrigation strategies in the wine industry. © 2017 Society of Chemical Industry.

Image analysis-based modelling for flower number estimation in grapevine.

BACKGROUND: Grapevine flower number per inflorescence provides valuable information that can be used for assessing yield. Considerable research has been conducted at developing a technological tool, based on image analysis and predictive modelling. However, the behaviour of variety-independent predictive models and yield prediction capabilities on a wide set of varieties has never been evaluated. RESULTS: Inflorescence images from 11 grapevine Vitis vinifera L. varieties were acquired under field conditions. The flower number per inflorescence and the flower number visible in the images were calculated manually, and automatically using an image analysis algorithm. These datasets were used to calibrate and evaluate the behaviour of two linear (single-variable and multivariable) and a nonlinear variety-independent model. As a result, the integrated tool composed of the image analysis algorithm and the nonlinear approach showed the highest performance and robustness (RPD = 8.32, RMSE = 37.1). The yield estimation capabilities of the flower number in conjunction with fruit set rate (R2 = 0.79) and average berry weight (R2 = 0.91) were also tested. CONCLUSION: This study proves the accuracy of flower number per inflorescence estimation using an image analysis algorithm and a nonlinear model that is generally applicable to different grapevine varieties. This provides a fast, non-invasive and reliable tool for estimation of yield at harvest. © 2016 Society of Chemical Industry.

Use of Visible and Short-Wave Near-Infrared Hyperspectral Imaging To Fingerprint Anthocyanins in Intact Grape Berries.

In red grape berries, anthocyanins account for about 50% of the skin phenols and are responsible for the final wine color. Individual anthocyanin levels and compositional profiles vary with cultivar, maturity, season, region, and yield and have been proposed as chemical markers to differentiate wines and to provide valuable information regarding the adulteration of musts and wines. A fast, easy, solvent-free, nondestructive method based on visible, short-wave, and near-infrared hyperspectral imaging (HSI) in intact grape berries to fingerprint the color pigments in eight different grape varieties was developed and tested against HPLC. Predictive models based on modified partial least-squares (MPLS) were built for 14 individual anthocyanins with coefficients of determination of cross-validation (R2CV) ranging from 0.70 to 0.93. For the grouping of total and nonacylated anthocyanins, external validation was conducted with coefficient of determination of prediction (R2P) of 0.86. HSI could potentially become an alternative to HPLC with reduced analysis time and labor costs while providing reliable and robust information on the anthocyanin composition of grape berries.

Data Mining and NIR Spectroscopy in Viticulture: Applications for Plant Phenotyping under Field Conditions.

Plant phenotyping is a very important topic in agriculture. In this context, data mining strategies may be applied to agricultural data retrieved with new non-invasive devices, with the aim of yielding useful, reliable and objective information. This work presents some applications of machine learning algorithms along with in-field acquired NIR spectral data for plant phenotyping in viticulture, specifically for grapevine variety discrimination and assessment of plant water status. Support vector machine (SVM), rotation forests and M5 trees models were built using NIR spectra acquired in the field directly on the adaxial side of grapevine leaves, with a non-invasive portable spectrophotometer working in the spectral range between 1600 and 2400 nm. The ν-SVM algorithm was used for the training of a model for varietal classification. The classifiers' performance for the 10 varieties reached, for cross- and external validations, the 88.7% and 92.5% marks, respectively. For water stress assessment, the models developed using the absorbance spectra of six varieties yielded the same determination coefficient for both cross- and external validations (R² = 0.84; RMSEs of 0.164 and 0.165 MPa, respectively). Furthermore, a variety-specific model trained only with samples of Tempranillo from two different vintages yielded R² = 0.76 and RMSE of 0.16 MPa for cross-validation and R² = 0.79, RMSE of 0.17 MPa for external validation. These results show the power of the combined use of data mining and non-invasive NIR sensing for in-field grapevine phenotyping and their usefulness for the wine industry and precision viticulture implementations.

Application of 2D and 3D image technologies to characterise morphological attributes of grapevine clusters.

BACKGROUND: Grapevine cluster morphology influences the quality and commercial value of wine and table grapes. It is routinely evaluated by subjective and inaccurate methods that do not meet the requirements set by the food industry. Novel two-dimensional (2D) and three-dimensional (3D) machine vision technologies emerge as promising tools for its automatic and fast evaluation. RESULTS: The automatic evaluation of cluster length, width and elongation was successfully achieved by the analysis of 2D images, significant and strong correlations with the manual methods being found (r = 0.959, 0.861 and 0.852, respectively). The classification of clusters according to their shape can be achieved by evaluating their conicity in different sections of the cluster. The geometric reconstruction of the morphological volume of the cluster from 2D features worked better than the direct 3D laser scanning system, showing a high correlation (r = 0.956) with the manual approach (water displacement method). In addition, we constructed and validated a simple linear regression model for cluster compactness estimation. It showed a high predictive capacity for both the training and validation subsets of clusters (R(2) = 84.5 and 71.1%, respectively). CONCLUSION: The methodologies proposed in this work provide continuous and accurate data for the fast and objective characterisation of cluster morphology. © 2016 Society of Chemical Industry.

Estimation of total soluble solids in grape berries using a hand-held NIR spectrometer under field conditions.

BACKGROUND: Recent studies have reported the potential of near infrared (NIR) spectral analysers for monitoring the ripeness of grape berries as an alternative to wet chemistry methods. This study covers various aspects regarding the calibration and implementation of predictive models of total soluble solids (TSS) in grape berries using laboratory and in-field collected NIR spectra. RESULTS: The performance of the calibration models obtained under laboratory conditions indicated that at least 700 berry samples are required to assure enough prediction accuracy. A statistically significant error reduction (ΔRMSECV = 0.1°Brix) with P < 0.001 was observed when measuring berries without epicuticular wax, which was negligible from a practical point of view. Under field conditions, the prediction errors (RMSEP = 1.68°Brix, and SEP = 1.67°Brix) were close to those obtained with the laboratory dataset (RMSEP = 1.42°Brix, SEP = 1.40°Brix). CONCLUSION: This work clarifies several methodological factors to develop a protocol for in-field assessing TSS in grape berries using an affordable, non-invasive, portable NIR spectral analyser. © 2015 Society of Chemical Industry.

Support Vector Machine and Artificial Neural Network Models for the Classification of Grapevine Varieties Using a Portable NIR Spectrophotometer.

The identification of different grapevine varieties, currently attended using visual ampelometry, DNA analysis and very recently, by hyperspectral analysis under laboratory conditions, is an issue of great importance in the wine industry. This work presents support vector machine and artificial neural network's modelling for grapevine varietal classification from in-field leaf spectroscopy. Modelling was attempted at two scales: site-specific and a global scale. Spectral measurements were obtained on the near-infrared (NIR) spectral range between 1600 to 2400 nm under field conditions in a non-destructive way using a portable spectrophotometer. For the site specific approach, spectra were collected from the adaxial side of 400 individual leaves of 20 grapevine (Vitis vinifera L.) varieties one week after veraison. For the global model, two additional sets of spectra were collected one week before harvest from two different vineyards in another vintage, each one consisting on 48 measurement from individual leaves of six varieties. Several combinations of spectra scatter correction and smoothing filtering were studied. For the training of the models, support vector machines and artificial neural networks were employed using the pre-processed spectra as input and the varieties as the classes of the models. The results from the pre-processing study showed that there was no influence whether using scatter correction or not. Also, a second-degree derivative with a window size of 5 Savitzky-Golay filtering yielded the highest outcomes. For the site-specific model, with 20 classes, the best results from the classifiers thrown an overall score of 87.25% of correctly classified samples. These results were compared under the same conditions with a model trained using partial least squares discriminant analysis, which showed a worse performance in every case. For the global model, a 6-class dataset involving samples from three different vineyards, two years and leaves monitored at post-veraison and harvest was also built up, reaching a 77.08% of correctly classified samples. The outcomes obtained demonstrate the capability of using a reliable method for fast, in-field, non-destructive grapevine varietal classification that could be very useful in viticulture and wine industry, either global or site-specific.

vitisFlower®: Development and Testing of a Novel Android-Smartphone Application for Assessing the Number of Grapevine Flowers per Inflorescence Using Artificial Vision Techniques.

Grapevine flowering and fruit set greatly determine crop yield. This paper presents a new smartphone application for automatically counting, non-invasively and directly in the vineyard, the flower number in grapevine inflorescence photos by implementing artificial vision techniques. The application, called vitisFlower(®), firstly guides the user to appropriately take an inflorescence photo using the smartphone's camera. Then, by means of image analysis, the flowers in the image are detected and counted. vitisFlower(®) has been developed for Android devices and uses the OpenCV libraries to maximize computational efficiency. The application was tested on 140 inflorescence images of 11 grapevine varieties taken with two different devices. On average, more than 84% of flowers in the captures were found, with a precision exceeding 94%. Additionally, the application's efficiency on four different devices covering a wide range of the market's spectrum was also studied. The results of this benchmarking study showed significant differences among devices, although indicating that the application is efficiently usable even with low-range devices. vitisFlower is one of the first applications for viticulture that is currently freely available on Google Play.

Assessment of cluster yield components by image analysis.

BACKGROUND: Berry weight, berry number and cluster weight are key parameters for yield estimation for wine and tablegrape industry. Current yield prediction methods are destructive, labour-demanding and time-consuming. In this work, a new methodology, based on image analysis was developed to determine cluster yield components in a fast and inexpensive way. RESULTS: Clusters of seven different red varieties of grapevine (Vitis vinifera L.) were photographed under laboratory conditions and their cluster yield components manually determined after image acquisition. Two algorithms based on the Canny and the logarithmic image processing approaches were tested to find the contours of the berries in the images prior to berry detection performed by means of the Hough Transform. Results were obtained in two ways: by analysing either a single image of the cluster or using four images per cluster from different orientations. The best results (R(2) between 69% and 95% in berry detection and between 65% and 97% in cluster weight estimation) were achieved using four images and the Canny algorithm. The model's capability based on image analysis to predict berry weight was 84%. CONCLUSION: The new and low-cost methodology presented here enabled the assessment of cluster yield components, saving time and providing inexpensive information in comparison with current manual methods.

Effects of UV exclusion on the physiology and phenolic composition of leaves and berries of Vitis vinifera cv. Graciano.

BACKGROUND: Ultraviolet (UV) radiation induces adaptive responses that can be used for plant production improvement. The aim of this study was to assess the effect of solar UV exclusion on the physiology and phenolic compounds of leaves and berry skins of Vitis vinifera L. cv. Graciano under field conditions. Phenolic compounds were analyzed globally and individually in both the vacuolar fraction and, for the first time in grapevine, the cell wall-bound fraction. These different locations may represent diverse modalities of phenolic response to and protection against UV. RESULTS: UV exclusion led to a decrease in Fv /Fm in leaves, revealing that solar UV is needed for adequate photoprotection. Only p-caffeoyl-tartaric acid from the soluble fraction of leaves and myricetin-3-O-glucoside from the soluble fraction of berry skins were significantly higher in the presence of UV radiation, and thus they could play a role in UV protection. Other hydroxycinnamic acids, flavonols, flavanols and stilbenes did not respond to UV exclusion. CONCLUSION: UV exclusion led to subtle changes in leaves and berry skins of Graciano cultivar, which would be well adapted to current UV levels. This may help support decision-making on viticultural practices modifying UV exposure of leaves and berries, which could improve grape and wine quality.

Solar ultraviolet radiation is necessary to enhance grapevine fruit ripening transcriptional and phenolic responses.

BACKGROUND: Ultraviolet (UV) radiation modulates secondary metabolism in the skin of Vitis vinifera L. berries, which affects the final composition of both grapes and wines. The expression of several phenylpropanoid biosynthesis-related genes is regulated by UV radiation in grape berries. However, the complete portion of transcriptome and ripening processes influenced by solar UV radiation in grapes remains unknown. RESULTS: Whole genome arrays were used to identify the berry skin transcriptome modulated by the UV radiation received naturally in a mid-altitude Tempranillo vineyard. UV radiation-blocking and transmitting filters were used to generate the experimental conditions. The expression of 121 genes was significantly altered by solar UV radiation. Functional enrichment analysis of altered transcripts mainly pointed out that secondary metabolism-related transcripts were induced by UV radiation including VvFLS1, VvGT5 and VvGT6 flavonol biosynthetic genes and monoterpenoid biosynthetic genes. Berry skin phenolic composition was also analysed to search for correlation with gene expression changes and UV-increased flavonols accumulation was the most evident impact. Among regulatory genes, novel UV radiation-responsive transcription factors including VvMYB24 and three bHLH, together with known grapevine UV-responsive genes such as VvMYBF1, were identified. A transcriptomic meta-analysis revealed that genes up-regulated by UV radiation in the berry skin were also enriched in homologs of Arabidopsis UVR8 UV-B photoreceptor-dependent UV-B -responsive genes. Indeed, a search of the grapevine reference genomic sequence identified UV-B signalling pathway homologs and among them, VvHY5-1, VvHY5-2 and VvRUP were up-regulated by UV radiation in the berry skin. CONCLUSIONS: Results suggest that the UV-B radiation-specific signalling pathway is activated in the skin of grapes grown at mid-altitudes. The biosynthesis and accumulation of secondary metabolites, which are appreciated in winemaking and potentially confer cross-tolerance, were almost specifically triggered. This draws attention to viticultural practices that increase solar UV radiation on vineyards as they may improve grape features.

Assessment of flower number per inflorescence in grapevine by image analysis under field conditions.

BACKGROUND: Flowers, flowering and fruit set are key determinants of grapevine yield. Currently, practical methods to assess the flower number per inflorescence, necessary for fruit set estimation, are time and labour demanding. This work aims at developing a simple, cheap, fast, accurate and robust machine vision methodology to be applied to RGB images taken under field conditions, to estimate the number of flowers per inflorescence automatically. RESULTS: Ninety images of individual inflorescences of Vitis vinifera L. cultivars Tempranillo, Graciano and Carignan were acquired in the vineyard with a pocket RGB camera prior to flowering, and used to develop and test the 'flower counting' algorithm. Strong and significant relationships, with R(2) above 80% for the three cultivars were observed between actual and automated estimation of inflorescence flower numbers, with a precision exceeding 90% for all cultivars. CONCLUSION: The developed algorithm proved that the analysis of digital images captured by pocket cameras under uncontrolled outdoors conditions was able to automatically provide a useful estimation of the number of flowers per inflorescence of grapevines at early stages of flowering.