BRAVE NEW TERROIR Grose, Thomas K
ASEE prism,
02/2019, Letnik:
28, Številka:
6
Journal Article
Two years ago, archaeologists discovered shards of clay vessels in the Caucasus region of Georgia that were covered in wine residue more than 8,000 years old. As the Washington Post reports, the ...finding boosted the former Soviet state's claim of being the birthplace of wine. Now, a group of Georgian entrepreneurs and academics want their country to create the first Martian wines and are working to develop grapevines for an eventual human colony there.
•Study aimed to detect Grapevine leafroll-associated virus 3 (GLRaV-3) at asymptomatic stage.•Datasets were hyperspectral images for three seasons and at five phenological stages within.•Spectral ...features were extracted using least absolute shrinkage and selection operator.•Six wavelengths (690, 715,731, 1409, 1425 and 1582 nm) were found sensitive to virus symptoms.
This research was conducted to examine the potential use of hyperspectral imaging for non-destructive detection of Grapevine leafroll-associated virus 3 (GLRaV-3) during asymptomatic and symptomatic stages of grapevine leafroll disease (GLD) in a red-berried wine grape (Vitis vinifera) cultivar. Cabernet Sauvignon vines tested positive and negative for GLRaV-3 were used in this study. Leaves from infected and non-infected vines were detached at five phenological stages and individual leaf images were acquired by a hyperspectral imager in 2017, 2018, and 2019 seasons. Those images were then preprocessed using spectra normalization and Monte-Carlo method for eliminating spectral sample outliers. Least absolute shrinkage and selection operator was used to select feature wavelengths for each phenological stage within three-season datasets. The sensitivity of selected feature wavelengths was evaluated based on analysis of variance and linear regression. Six salient wavelengths (690, 715, 731, 1409, 1425 and 1582 nm) were determined as sensitive wavebands for detecting virus symptoms in leaf samples. The detectability of GLD using those six salient wavelengths was evaluated using least squares-support vector machine. The classification accuracy was found between 66.67 and 89.93% for test datasets collected at first asymptomatic stage over three seasons. These results indicated that the hyperspectral imaging technique has the potential for nondestructively detecting virus-infected grapevines during asymptomatic stages.
Several virus diseases cause damage to Vitis vinifera L., but information on their incidence and impact on hybrid cultivars is scarce, particularly under cool-climate conditions. In Nova Scotia (NS), ...the wine industry is based predominantly on interspecific hybrid cultivars. To understand the occurrence of major grapevine viruses in NS, surveys were conducted in 2016, 2017 and 2018. A total of 965 composite five-vine samples, collected from 35 hybrids and 18 V. vinifera vineyard blocks, were tested for grapevine leafroll-associated virus 1 (GLRaV-1), GLRaV-2, GLRaV-3 and GLRaV-4, grapevine fanleaf virus (GFLV), grapevine red blotch virus (GRBV) and grapevine Pinot gris virus (GPGV) by PCR/RT-PCR. Overall, 3.4% of the samples were positive for GLRaV-1, 22.8% for GLRaV-3, 0.9% for GFLV, 4.6% for GRBV and 3.2% for GPGV. None of the 575 samples collected in 2016 and 2017 tested positive for GLRaV-2 or GLRaV-4. Mixed infections by more than one virus occurred in 3% of the composite samples. Of 671 hybrid and 294 V. vinifera samples tested, 38.3% and 27.6% were positive for at least one of the viruses (GLRaV-1, −3, GFLV, GRBV and GPGV), respectively. Phylogenetic analysis of GLRaV-1, −3, GFLV and GPGV revealed the presence of global variants. Complete genome characterization and phylogenetic analysis of nine GRBV isolates grouped three into clade I and six into clade II, indicating the presence of two variants. These findings, along with preliminary reports of insect vectors, establish the first epidemiological framework of the major viral diseases in NS, highlighting the need for long-term management strategies.
Grapevine leafroll-associated virus 3
(GLRaV-3) is the most prevalent and destructive virus species that contributes to grapevine leafroll disease, an economically damaging disease that affects ...vineyards globally.
Grapevine virus A
(GVA) is a virus species in the rugose wood complex and is associated with several vineyard diseases. Both virus species are transmitted by several mealybug species. Transmission efficiency is a major facet of pathogen spread and may be influenced by virus species interactions in the vector or host. We tested transmission efficiency of GLRaV-3 and GVA from nine field-collected source vine samples of
Vitis vinifera
cv Chardonnay by first instars of
Planococcus ficus
. Transmission of GLRaV-3 was 22% greater than transmission of GVA. Establishment of new mixed GLRaV-3/GVA infections did not differ significantly from single GLRaV-3 infections following inoculation by
P. ficus
. These results suggest that GVA may have a higher likelihood of establishing new infections in concert with GLRaV-3 than in single infections.
Climate change is a continuous spatiotemporal reality, possibly endangering the viability of the grapevine (Vitis vinifera L.) in the future. Europe emerges as an especially responsive area where the ...grapevine is largely recognised as one of the most important crops, playing a key environmental and socio-economic role. The mounting evidence on significant impacts of climate change on viticulture urges the scientific community in investigating the potential evolution of these impacts in the upcoming decades. In this review work, a first attempt for the compilation of selected scientific research on this subject, during a relatively recent time frame (2010–2020), is implemented. For this purpose, a thorough investigation through multiple search queries was conducted and further screened by focusing exclusively on the predicted productivity parameters (phenology timing, product quality and yield) and cultivation area alteration. Main findings on the potential impacts of future climate change are described as changes in grapevine phenological timing, alterations in grape and wine composition, heterogeneous effects on grapevine yield, the expansion into areas that were previously unsuitable for grapevine cultivation and significant geographical displacements in traditional growing areas. These compiled findings may facilitate and delineate the implementation of effective adaptation and mitigation strategies, ultimately potentiating the future sustainability of European viticulture.
Wine is the most important product from the Douro Region, in Portugal. Ampelographs are disappearing, and farmers need new solutions to identify grapevine varieties to ensure high-quality standards. ...The development of methodology capable of automatically identify grapevine are in need. In the scenario, deep learning based methods are emerging as the state-of-art in grapevines classification tasks. In previous work, we verify the deep learning models would benefit from focus classification patches in leaves images areas. Deep learning segmentation methods can be used to find grapevine leaves areas.
This paper presents a methodology to segment grapevines images automatically based on the U-net model. A private dataset was used, composed of 733 grapevines images frames extracted from 236 videos collected in a natural environment. The trained model obtained a Dice of 95.6% and an Intersection over Union of 91.6%, results that fully satisfy the need of localise grapevine leaves.
Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially ...detect and quantify viral diseases in a nondestructive manner. This study utilized hyperspectral imagery at the plant level to identify and classify grapevines inoculated with the newly discovered DNA virus grapevine vein-clearing virus (GVCV) at the early asymptomatic stages. An experiment was set up at a test site at South Farm Research Center, Columbia, MO, USA (38.92 N, -92.28 W), with two grapevine groups, namely healthy and GVCV-infected, while other conditions were controlled. Images of each vine were captured by a SPECIM IQ 400-1000 nm hyperspectral sensor (Oulu, Finland). Hyperspectral images were calibrated and preprocessed to retain only grapevine pixels. A statistical approach was employed to discriminate two reflectance spectra patterns between healthy and GVCV vines. Disease-centric vegetation indices (VIs) were established and explored in terms of their importance to the classification power. Pixel-wise (spectral features) classification was performed in parallel with image-wise (joint spatial-spectral features) classification within a framework involving deep learning architectures and traditional machine learning. The results showed that: (1) the discriminative wavelength regions included the 900-940 nm range in the near-infrared (NIR) region in vines 30 days after sowing (DAS) and the entire visual (VIS) region of 400-700 nm in vines 90 DAS; (2) the normalized pheophytization index (NPQI), fluorescence ratio index 1 (FRI1), plant senescence reflectance index (PSRI), anthocyanin index (AntGitelson), and water stress and canopy temperature (WSCT) measures were the most discriminative indices; (3) the support vector machine (SVM) was effective in VI-wise classification with smaller feature spaces, while the RF classifier performed better in pixel-wise and image-wise classification with larger feature spaces; and (4) the automated 3D convolutional neural network (3D-CNN) feature extractor provided promising results over the 2D convolutional neural network (2D-CNN) in learning features from hyperspectral data cubes with a limited number of samples.
•We investigate Cu tolerance and accumulation in Vitis vinifera ssp. sylvestris.•Effective concentration was higher in wild grapevine than in 41B rootstock.•Wild grapevine can be considered a ...Cu-tolerant subspecies of Vitis vinifera.•Plants from the contaminated site are more efficient in controlling root Cu content.
We evaluate copper tolerance and accumulation in Vitis vinifera ssp. sylvestris in populations from a copper contaminated site and an uncontaminated site, and in the grapevine rootstock “41B”, investigating the effects of copper (0–23mM) on growth, photosynthetic performance and mineral nutrient content. The highest Cu treatment induced nutrient imbalances and inhibited photosynthetic function, causing a drastic reduction in growth in the three study plants. Effective concentration was higher than 23mM Cu in the wild grapevines and around 9mM in the “41B” plants. The wild grapevine accessions studied controlled root Cu concentration more efficiently than is the case with the “41B” rootstock and must be considered Cu-tolerant. Wild grapevines from the Cu-contaminated site present certain physiological characteristics that make them relatively more suitable for exploitation in the genetic improvement of vines against conditions of excess Cu, compared to wild grapevine populations from uncontaminated sites.
•Leaf level VIS–NIR sensing was assessed for grapevine GLRaV–3 detection.•Salient feature wavelengths were 1001, 1027 and 1052 nm.•Features were robust to detect GLRaV–3 symptoms at asymptomatic ...stage.•QDA performed better than Naïve Bayes in classifying infected samples.
Grapevine leafroll disease (GLD) is one of the major threats to wine grapes (Vitis vinifera) causing substantial economic losses to the growers. This study was undertaken to evaluate the applicability of visible and near infrared (VIS-NIR) spectroradiometery as a rapid, robust and non–destructive optical sensing method for the detection of Grapevine leafroll-associated virus 3 (GLRaV-3) at different phenological stages in a red-berried wine grape cultivar. Using VIS-NIR spectroradiometer, data was collected from the healthy and GLRaV-3-infected leaf samples from cv. Cabernet Sauvignon for two seasons at specific intervals during asymptomatic and symptomatic stages of the disease. Fiber optic leaf clip was used to collect spectral responses from grapevine leaves under field conditions. Salient feature extraction using stepwise multilinear regression and partial least square regression methods showed significant differences between healthy and virus–infected leaves in the visible (351, 377, 501, 526, 626 and 676 nm) and near infrared (701, 726, 826, 901, 951, 976, 1001, 1027, 1052 and 1101 nm) regions. Spectral wavelengths from near infrared region (1001, 1027 and 1052 nm) were validated at different phenological stages spanning both asymptomatic and symptomatic stages of the disease. Selected spectral wavelengths demonstrated robustness in virus detection with overall classification accuracies in the range of 75–99% using quadratic discriminant analysis (QDA) classifier. QDA based classification accuracies for healthy, infected and overall classes were significantly higher compared to Naïve Bayes classifier. The accuracy for virus detection during asymptomatic stages was not significantly different from the symptomatic phase, indicating reliability of the selected features for early detection of GLRaV–3–infected grapevines.
Herein, we described a novel plasmonic CRISPR Cas12a assay for the visual, colorimetric detection of grapevine viral infections. Our assay generates rapid and specific colorimetric signals for ...nucleic acid amplicons by combining the unique target-induced incriminate single-stranded DNase activity of Cas12a with plasmon coupling of DNA functionalized gold nanoparticles. The practical applicability of our plasmonic assay was successfully demonstrated through the detection of emerging red-blotch viral infections in grapevine samples collected from commercial vineyards.
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