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392 results for “grape”
InnoVine WP3: 105 phenolic compound quantification of 2014 and 2015 mature grape berries from a core-collection of 279 irrigated and non-irrigated Vitis vinifera cultivars
<p>FP7/311775 InnoVine (Innovation in vineyard): Combining innovation in vineyard management and genetic diversity for a sustainable European viticulture</p> <p>WP3: Exploiting the genetic diversity in grapevine</p> <p>105 phenolic or related compounds, from 2014 and 2015 mature grape berries from a core-collection of 279 irrigated and non-irrigated <em>Vitis vinifera</em> cultivars, were quantified by UPLC-TQ-MRM Mass Spectrometry (Lambert M<em> et al., Molecules</em> <strong>2015</strong>, <em>20</em>(5), 7890-7914; doi:10.3390/molecules20057890 & Pinasseau L <em>et al.</em>, <em>Molecules</em> <strong>2016</strong>, <em>21</em>(10), 1409; doi:10.3390/molecules21101409).</p> <p>3 parameters were added:<br> - water/drought status (delta C13)<br> - sugar content (refractive index, brix degree)<br> - weight of 100 grape berries</p> <p>All plant material was collected at the Vassal repository: French National Grapevine Germplasm Collection, INRA Domaine de Vassal, 34340 Marseillan-Plage, France (Centre de Ressources Biologiques de la Vigne (CRB-Vigne) de Vassal-Montpellier).</p>
Spectral and Chemical Dataset for Ripeness Monitoring in cv. Tempranillo Grapes Using a Multispectral Sensor
<p><strong><span>The dataset consists of 1010 samples of Tempranillo grape berries, offering a comprehensive record of spectral and chemical measurements that serve as a valuable resource for evaluating berry ripeness and sugar content (ºBrix). Each row in the dataset corresponds to a single berry, and the columns include a unique identifier (ID), the date of sampling (spanning 21 different days during the ripening period in 2024), expressed as Day of the Year (DOY) from DOY 210 to DOY 284. The dataset also includes measurements for nine spectral bands which represent the reflectance values recorded by the sensor (F1–F8 and NIR), a dedicated channel to detect ambient light flicker (CLEAR), and the sugar content (</span><span>°Bx</span><span>), ranging from 4.8 to 45 </span><span>°Bx</span><span>, encompassing all maturity stages from early ripeness to over-ripeness. The dataset is structured so that rows correspond to individual berries, and columns represent the measured variables, enabling statistical and machine learning analyses</span></strong></p> <p><strong><span>Center wavelength (λp) (F1: 415 nm, F2: 445 nm, F3: 480nm, F4: 515nm, F5: 555nm, F6: 590nm, F7: 630nm, F8: 680nm)</span></strong></p>
AI4Agriculture Grape Dataset
<p>The AI4Agriculture Grape Dataset is a collection of 250 images taken in a vineyard in Ribera de Duero. The grapefruits are from the Tempranillo variety. The images are annotated using bounding boxes. The goal of this dataset is to provide images and the associated annotations to train and validate object detection models. These models can be used in viticulture applications, such as yield estimation, fruit counting, and field robotics.</p> <p>The images were taken in twelve different parcels. Six areas were defined in each parcel. Each area contains several lines of plants (grapevines). For each line of plants, 4 picture samples have been taken (two on each side). The samples are from non-contiguous grapevines, so the pictures never overlap. </p> <p>The pictures were taken by a Xiaomi Redmi 8 (with resolution 4032x3024), Xiaomi Mi A3 (with resolution 3000x4000) and a Xiaomi Redmi 9 (with resolution 3264x2448). To avoid showing grapes from other rows, the pictures were taken at approximately half the height of the vine-tree rows.</p> <p>For each image, a .xml file was generated, with the Pascal VOC format, containing all the labeled items (with bounding boxes) in that image. Annotations in YOLO format are also provided (*.txt). The labeling methodology is described in <a href="https://drive.google.com/file/d/1k5r4mMRD8123N7q-4D3APnwkpER8G6lw/view?usp=sharing">this document</a>.</p> <p>Some images have issues: i) reflections caused by the sun ii) group of grapes partially hidden by leaves or iii) hidden by other objects (e.g. irrigation pipes), iv) color and direction similar to a tree trunk v) dead grapes in the floor or vi) grapes from the plant behind the plant analyzed.</p> <p>The images were captured by SmartRural and annotated by ATOS, UPC, Deutsches Zentrum für Luft- und Raumfahrt, and National and Kapodistrian University of Athens in the framework of the AI4Agriculture pilot of the AI4EU project.</p>
Supporting data: HiFi chromosome-scale diploid assemblies of the grape rootstocks 110R, Kober 5BB, and 101-14 Mgt
<p>Repository for supporting data to the paper: HiFi chromosome-scale diploid assemblies of the grape rootstocks 110R, Kober 5BB, and 101-14 Mgt</p>
Embrapa Wine Grape Instance Segmentation Dataset – Embrapa WGISD
<p><strong>Embrapa Wine Grape Instance Segmentation Dataset – Embrapa WGISD</strong></p> <p>For a detailed description of this dataset, following the <em>datasheet for the datasets</em> recommendation proposed by <a href="https://arxiv.org/abs/1803.09010">Gebru et al.</a>, check the <strong>README.md</strong> file.</p> <p><strong>Motivation for Dataset Creation</strong></p> <p><em>Why was the dataset created?</em></p> <p>Embrapa WGISD (<em>Wine Grape Instance Segmentation Dataset</em>) was created to provide images and annotation to study <em>object detection and instance segmentation</em> for image-based monitoring and field robotics in viticulture. It provides instances from five different grape varieties taken on field. These instances shows variance in grape pose,<br> illumination and focus, including genetic and phenological variations such as shape, color and compactness.</p> <p><em>What (other) tasks could the dataset be used for?</em></p> <p>Possible uses include relaxations of the instance segmentation problem: classification (Is a grape in the image?), semantic segmentation (What are the "grape pixels" in the image?), and object detection (Where are the grapes in the image?). The WGISD can also be used in grape variety identification.</p> <p> </p>
CERTH Grape Dataset
<p>The CERTH grape dataset aims to advance computer vision and machine learning research in the field of viticulture by providing valuable annotated data for developing and refining algorithms for accurate grape segmentation, yield prediction, and, most importantly, maturity estimation. The dataset consists of 2502 high-resolution images captured from a vineyard cultivating the 'Crimson Seedless' table grape variety during the 2022–2023 development and harvesting period and exhibits different view angles, camera focus conditions, and illumination variations. The images are captured using an iPhone 11 Pro smartphone and are scaled to a resolution of 2160 × 3840 pixels.</p><p>The annotations include grape bunches with detailed object masks and are categorized into three distinct classes (i.e., immature, semi-mature, and mature) based on the degree of grape maturity, as identified by the color of the grapes in the bunch. As a result, grapes in the immature class were early in their development phase, grapes in the mature class were close to the harvesting season, and grapes in the semi-mature class were in the intermediate period when changes in the color of the grapes from yellow to red had initiated.</p><p>The CERTH grape dataset consists of 9832 labeled grape bunches, extracted from all 2502 images. The grape images are split into training, validation, and test sets, consisting of 2000, 251, and 251 images, respectively. The training set comprises 7959 annotated grape bunches, while the validation and test sets comprise 914 and 959 annotated grape bunches, respectively.</p><p>The CERTH grape datasets can be evaluated on two scenarios. The multi-instance / multi-class (mimc) scenario evaluates the performance of algorithms in multi-instance classification using the original test set, while the single-instance / one-class (sioc) scenario extracts 100 images from the original test set that contain a single grape bunch per image to evaluate the performance of algorithms in single-instance classification. The sioc subset consists of 36, 43, and 21 images that depict grape bunches from the immature, semi-mature, and mature classes, respectively.</p><p>To use this dataset, please also cite:</p><p>Blekos, A.; Chatzis, K.; Kotaidou, M.; Chatzis, T.; Solachidis, V.; Konstantinidis, D.; Dimitropoulos, K. A Grape Dataset for Instance Segmentation and Maturity Estimation. Agronomy 2023, 13, 1995. https://doi.org/10.3390/agronomy13081995.</p>
December 2021 eclipse monitoring of WWV at 10 MHz using Grape 1
<p>Ten days of fldigi recordings in HamSci Grape 1 format of WWV at 10 MHz around the December 2021 eclipse</p>
X-ray imaging of 30 year old wine grape wood reveals cumulative impacts of rootstocks on scion secondary growth and harvest index
<p><span></span></p> <p><span>Annual rings from vines in a 30 year old, California rootstock trial were measured to determine the effects of 15 different rootstocks on Chardonnay and Cabernet Sauvignon scions. Viticultural traits measuring vegetative growth, yield, berry quality, and nutrient uptake were measured at the beginning and end of the lifetime of the vineyard.</span></p> <p><span>X-ray Computed Tomography (CT) was used to measure ring widths in 103 vines. Ring width was modeled as a function of ring number using a negative exponential model. Early and late wood ring widths, cambium width, and scion trunk radius were correlated with 27 traits. </span></p> <p><span>Modeling of annual ring width shows that scions alter the width of the first rings but that rootstocks alter the decay thereafter, consistently shortening ring width throughout the lifetime of the vine. The ratio of yield to vegetative growth, juice pH, photosynthetic assimilation and transpiration rates, and stomatal conductance are correlated with scion trunk radius.</span></p> <p><span>Rootstocks modulate secondary growth over years, altering hydraulic conductance, physiology, and agronomic traits. Rootstocks act in similar but distinct ways from climate to modulate ring width, which borrowing techniques from dendrochronology, can be used to monitor both genetic and environmental effects in woody perennial crop species.</span></p>
wGrapeUNIPD-DL: an open dataset for white grape bunch detection
<p>National and international Vitis variety catalogues can be used as image datasets for computer vision in viticulture. These databases archive ampelographic features and phenology of several grape varieties and plant structures images (e.g. leaf, bunch, shoots). Although these archives represent a potential database for computer vision in viticulture, plant structure images are acquired singularly and mostly not directly in the vineyard. Localization computer vision models would take advantage of multiple objects in the same image, allowing more efficient training. The present images and labels dataset was designed to overcome such limitations and provide suitable images for multiple cluster identification in white grape varieties. A group of 373 images were acquired from later view in vertical shoot position vineyards in six different Italian locations at different phenological stages. Images were then labelled in YOLO labelling format. The dataset was made available both in terms of images and labels. The real number of bunches counted in the field, and the number of bunches visible in the image (not covered by other vine structures) was recorded for a group of images in this dataset.</p>
Tonga Eruption Grape Data by AB4EJ
<p>What is on these plots: the upper trace is the waterfall showing spectrum 10 Hz wide (“narrow”). The strongest signal has been emphasized in red. The lower trace is relative amplitude (unitless and non-calibrated).</p> <ol> <li>2022-01-14 – the day before the explosion, as a baseline. In the upper left, you see the standard deviation of the spectrum data for the day, which I use as a measure of ionospheric activity.</li> <li>2022-01-15 – day of the explosion – according to various papers, a wave came through North America after 1400Z (if I understand correctly). Note the higher standard deviation for the day, which shows a combination of the Tonga pulse plus higher geomagnetic activity that day.</li> <li>2022-01-16 – day after the explosion, things are back to normal; standard deviation back down to around 14.</li> </ol> <p> </p> <p>HOW TO REPRODUCE THESE PLOTS:</p> <ol> <li>Download the Box data to your local drive; ensure that it has a directory structure as shown in the .emz file (in this example, you have downloaded to the D: drive).</li> </ol> <p>The most critical thing is that the directory structure must be what the program expects; it should already be set up this way in the ch0 folder.</p> <p> </p> <p>Set the variables in the program:</p> <p>dataDir = "D:\\Tonga_AB4EJ"</p> <p>metadata_dir = dataDir + '\\ch0\\metadata'</p> <p> </p> <p>The variable dataDir must point at the directory <em>above</em> the one that contains ch0.</p> <p>The variable metadata_dir must point at the directory that contains the file drf_properties.h5 .</p> <p> </p> <p>Using the program</p> <pre>Run python program plotspectrum.py. (Note that you must have Digital RF installed. Best way to do this is to work in a virtual environment, then do: pip install digital_rf ) </pre> <p>Program will prompt for a date. To see Jan 14, here enter: 2022-01-14</p> <p>You should get a plot like shown under point 1, above. Note that the program is quite slow and may take 5 – 10 minutes to read the file & create the plot.</p> <p>If you get the following error…</p> <p>ValueError: No channels found: top_level_directory_arg = C:\Users\bengelke\Box\share\Tonga_AB4EJ1. If path is correct, you may need to run recreate_properties_file to re-create missing drf_properties.h5 files.</p> <p>… it usually means that (a) the directory structure is not exactly correct, and/or (b) there is a typo in one of the directory settings dataDir or metadataDir.</p> <p> </p>
Fig. 2 in Alien grapes (Vitis, Vitaceae) in Sicily (Italy): novelties for the Sicilian and Mediterranean flora
Fig. 2 - Geographical distribution in Sicily of Vitis labrusca (green dots), V. ×instabilis (red dots), and V. ×koberi (blue dots).
Fig. 5 in Alien grapes (Vitis, Vitaceae) in Sicily (Italy): novelties for the Sicilian and Mediterranean flora
Fig. 5 - Rootstocks of Vitis ×ruggerii '140 Ru' emerging from an abandoned vineyard in �cate (Photo: N.M.G. �rdenghi).
Fig. 1 in Alien grapes (Vitis, Vitaceae) in Sicily (Italy): novelties for the Sicilian and Mediterranean flora
Fig. 1 - Geographical distribution in Sicily of Vitis rupestris (red dots), Vitis ×goliath (green dots), and Vitis ×ruggerii (blue dots).
Fig. 4 in Alien grapes (Vitis, Vitaceae) in Sicily (Italy): novelties for the Sicilian and Mediterranean flora
Fig. 4 - Invasive stand of Vitis ×ruggerii '775 P' in �cate, with the first author posing for scale. The plant completely covers the fence of an olive grove and, on the extreme right, an almond tree (Photo: P. Cauzzi).
Fig. 3 in Alien grapes (Vitis, Vitaceae) in Sicily (Italy): novelties for the Sicilian and Mediterranean flora
Fig. 3 - Vitis ×koberi in Sutera: a) invasive stand along the railway; b) leaf blades (Photos: N.M.G. Ardenghi).
Fig. 7 in Alien grapes (Vitis, Vitaceae) in Sicily (Italy): novelties for the Sicilian and Mediterranean flora
Fig. 7 - Vitis rupestris: a) invasive monospecific stand in Mezzojuso; b) leaf blades (Photos: N.M.G. �rdenghi).
Fig. 6 in Alien grapes (Vitis, Vitaceae) in Sicily (Italy): novelties for the Sicilian and Mediterranean flora
Fig. 6 - Vitis ×ruggerii: a) typical entire leaf blades; b-c) cultivar '57 R' with lobed leaf blades (Photos: N. M. G. �rdenghi).
Figure 1 in First record of the grape cane borer, Amphicerus bimaculatus (Olivier, 1790) (Coleoptera: Bostrichidae), on pomegranate in Greece
Figure 1. Satellite photo of the study area. Red pin points to the Vrontou area, where the pomegranate orchard is situated (Copyright 2015 Google; Image Copyright 2015 CNES/Astrium). In the smaller map of Greece, the Pieria prefecture is marked in red color (Copyright Wikipedia).
Fig. 2 in Biology and fertility life table of Bactrocera carambolae on grape and acerola
Fig. 2. Survival curves of Bactrocera carambolae adults grown on grapes (Vitis vinifera) and acerola (Malpighia emarginata) in laboratory (26 ± 2 ◦C; 60 ± 10% RH; photophase 12 h).
Fig. 5 in Establishment of the sea grape flatid, Petrusa epilepsis (Hemiptera: Fulgoroidea: Flatidae), in Florida
Fig. 5. Feeding damage of Petrusa epilepsis on Stachytarpheta jamaicensis (blue porterweed) (A) and Hamelia patens (firebush) (B).
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.