Appraisal of wine color and phenols from a non-invasive grape berry fluorescence method

[Aims] To investigate the relationships between the anthocyanin content of Vitis vinifera L. cvs. Tempranillo and Graciano grapes, determined in the vineyard and in the winery by a non-invasive fluorescence sensor, and the final wine color and phenolic traits.[Methods and results] Grape anthocyanin and phenol measurements were conducted with a hand-held, non-destructive fluorescence-based proximal sensor, in the vineyard (on clusters hanging on the vine) and at the winery (on harvested clusters in boxes) in two seasons. The anthocyanin fluorescence indices, ANTHRG and FERARI, were found to significantly correlate with the wine color density (R2 ranged from 0.51 to 0.82) and total phenol index (R2 ranged from 0.44 to 0.87), regardless that the measurements were made in the vineyard or in the winery. Similarly, the CIELAB parameters defining lightness (L*), hue angle (h*) and coordinate b* (yellow-blue component of the wine tonality) also showed significant relationships (R2 ranged from 0.55 to 0.74) with ANTHRG and FERARI indices in very young wines.[Conclusion] This preliminary study showed that satisfactory estimation of the final wine color and phenolic characteristics can be obtained from fast, non-destructive measurements in grapes, using a fluorescence-based sensor, either in the vineyard or in the winery.[Significance and impact of the study] This is the first work showing the capabilities of the chlorophyll fluorescence of grapes to estimate the final wine color and phenolic traits. This information could help the wine industry make more informed decisions regarding selective harvest and winemaking in a fast and cost-effective way. © Vigne et Vin Publications Internationales (Bordeaux, France).The authors want to thank Force-A and its team for their help and financial support.Peer Reviewe

Infra-Red thermal image analysis for grapevines

Trabajo presentado en el 18th International Symposium of the Group of International Experts of vitivinicultural Systems for CoOperation (GIESCO 2013), celebrado en Oporto del 7 al 11 de julio de 2013.-- Número fuera de serie.Infrared thermal images (IRTI) have been used for grapevine research since the early 90’s. Even though its promising results in the assessment of canopy stomatal conductance and plant water status, from the beginning and recent research publications, it has not been fully applied on a commercial scale yet. It is believed that the bottleneck for this technology is the lack of reliable automation tools for IRTI analysis. Accurate and reliable automation technique s will allow the use of this technique to assess the spatial variability of physiological processes within the canopy using infrared cameras mounted on moving vehicles, drones, octocopters or robots. Automated analysis systems are requirement of The Vineyard of The Future initiative, which is an international effort to establis h fully monitored vineyards in the most prominent viticultural and winemaking areas in the world. In this work, a semi-automated IRTI analyses performed using a code written in MATLAB® for estimate dry and wet references excluding non-leaf temperatures was compared with evaporimeter (EvapoSensor, Skye Instruments Ltd, Powys, UK) measurements used to provide dry and wet references from IRTIs. Results obtained from this research (grapevines cv. Tempranillo) showed good and statistically significant correlations between temperatur e references obtained from IRTI analysis and measured values. This work constitutes one additional step forward to the implementation of thermal imaging as an automated routine technique for physiological vineyard assess ment from proximal sensing and unmanned aerial vehicles (UAV) platforms.The research leading to this report was supported by the Spanish project “STRESSIMAGING HPRN-CT-2002-00254” and Chilean projects CONICYT (Nº 79090035) and Programa de Investigación sobre Adaptación de la Agricultura al Cambio Climático - PIEI (Universidad de Talca).Peer Reviewe

A Comparison of Fuzzy Clustering Algorithms Applied to Feature Extraction on Vineyard

Image segmentation is a process by which an image is partitioned into regions with similar features. Many approaches have been proposed for color image segmentation, but Fuzzy C-Means has been widely used, because it has a good performance in a large class of images. However, it is not adequate for noisy images and it also takes more time for execution as compared to other method as K-means. For this reason, several methods have been proposed to improve these weaknesses. Method like Possibilistic C-Means, Fuzzy Possibilistic C-Means, Robust Fuzzy Possibilistic C-Means and Fuzzy C-Means with Gustafson-Kessel algorithm. In this paper we perform a comparison of these clustering algorithms applied to feature extraction on vineyard images. Segmented images are evaluated using several quality parameters such as the rate of correctly classied area and runtim

Pixel classification through Mahalanobis distance for identification of grapevine canopy elements on RGB images

Vine vigour and fruit-cluster exposure to sunlight in a grapevine canopy fruiting zone has been shown to strongly correlate with key fruit composition and diseases incidence. In this framework, the use of automated image analysis for the identification of plant elements is an important issue to be addressed for vineyard assessment (Dunn and Martin, 2004). In addition, optimum segmentation method is strongly application dependent and thus needs to be tested for each particular case (Cheng et al., 2001). The objective of the present work is to propose and test a simple, rapid and practical method for the identification of two relevant elements of grapevines canopy: clusters and green leaves

MAGIC Upper Limits for two Milagro-detected, Bright Fermi Sources in the Region of SNR G65.1+0.6

We report on the observation of the region around supernova remnant G65.1+0.6 with the stand-alone MAGIC-I telescope. This region hosts the two bright GeV gamma-ray sources 1FGL J1954.3+2836 and 1FGL J1958.6+2845. They are identified as GeV pulsars and both have a possible counterpart detected at about 35 TeV by the Milagro observatory. MAGIC collected 25.5 hours of good quality data, and found no significant emission in the range around 1 TeV. We therefore report differential flux upper limits, assuming the emission to be point-like (<0.1 deg) or within a radius of 0.3 deg. In the point-like scenario, the flux limits around 1 TeV are at the level of 3 % and 2 % of the Crab Nebula flux, for the two sources respectively. This implies that the Milagro emission is either extended over a much larger area than our point spread function, or it must be peaked at energies beyond 1 TeV, resulting in a photon index harder than 2.2 in the TeV band.Comment: 8 pages, 3 figures, 1 tabl

New plant phenotyping technologies in a changing climate

Trabajo presentado en el ClimWine (International Symposium on Sustainable grape and wine production in the context of climate change), celebrado en Burdeos del 10 al 13 de abril de 2016.Peer Reviewe

Immediate Versus Conventional Loading of Complete-Arch Implant-Supported Prostheses in Mandibles with Failing Dentition: A Patient-Centered Controlled Prospective Study

Purpose: The aim of this study was to compare, from the patients' perspective, immediate and conventional loading of fixed complete-arch prostheses to rehabilitate mandibles with failing dentition. Materials and Methods: This controlled, prospective, nonrandomized study included 36 consecutive patients: 18 treated with conventional loading (control) and 18 with immediate loading (test). Patient general satisfaction and specific satisfaction with esthetics, chewing, speaking, comfort, self-esteem, ease of cleaning, and treatment duration were evaluated using 10-cm visual analog scales before treatment and 3 and 12 months after treatment. Postoperative pain and swelling were monitored daily for 1 week. Statistical analysis was performed applying Mann-Whitney and Wilcoxon tests (alpha =.05). Results: Between baseline and 3 months, satisfaction in the test group increased significantly with the exception of speech; in the control group, satisfaction increased significantly for esthetics and decreased significantly for speech, chewing, and comfort, but did not vary for general satisfaction or self-esteem. After 3 months, satisfaction was significantly higher in the test group with the exception of ease of cleaning. Between 3 and 12 months, satisfaction improved in both groups but more so in the control group, so that after 12 months there were no differences. The test group showed lower mean pain, which began after the third day postsurgery. Mean swelling and maximum pain/swelling did not show significant differences at any point. Conclusions: Patient satisfaction was reported as significantly higher with immediate loading. However, at the end of the observation periods, reported functional differences had disappeared. Significant differences were only noted for postoperative pain after the third day

Grapevine Yield and Leaf Area Estimation Using Supervised Classification Methodology on RGB Images Taken under Field Conditions

The aim of this research was to implement a methodology through the generation of a supervised classifier based on the Mahalanobis distance to characterize the grapevine canopy and assess leaf area and yield using RGB images. The method automatically processes sets of images, and calculates the areas (number of pixels) corresponding to seven different classes (Grapes, Wood, Background, and four classes of Leaf, of increasing leaf age). Each one is initialized by the user, who selects a set of representative pixels for every class in order to induce the clustering around them. The proposed methodology was evaluated with 70 grapevine (V. vinifera L. cv. Tempranillo) images, acquired in a commercial vineyard located in La Rioja (Spain), after several defoliation and de-fruiting events on 10 vines, with a conventional RGB camera and no artificial illumination. The segmentation results showed a performance of 92% for leaves and 98% for clusters, and allowed to assess the grapevine’s leaf area and yield with R2 values of 0.81 (p < 0.001) and 0.73 (p = 0.002), respectively. This methodology, which operates with a simple image acquisition setup and guarantees the right number and kind of pixel classes, has shown to be suitable and robust enough to provide valuable information for vineyard management