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208 results for “Blueberry”
FlexiGroBots - Blueberry orchard UAV dataset
<p>FlexiGroBots - Blueberry orchard UAV dataset</p> <p>Acquisition date: 02.07.2021.<br> Location: Babe, Serbia</p> <p>Dataset consists of UAV drone images:<br> - 6 channels: reflectance blue, reflectance green, reflectance red, reflectance red edge, reflectance NIR, RGB.</p> <p>In order to align different channels, registration was applied and because of uneven illumination during the acquisition process, illumination correction was performed. Thus, the results of each preprocessing step are located in separate folders, i.e. raw data are in 100FPLAN and 101FPLAN, results of registration are in 100FPLAN_registrated and 101FPLAN_registrated, while images with corrected illumination are in 100FPLAN_registrated_corrected and 101FPLAN_registrated_corrected.</p> <p>Orthomosaics created before and after preprocessing are located in UAV orthomosaics folder.</p>
FlexiGroBots - Blueberry UAV Hyperspectral Dataset
<p>Acquisition dates: 07.07.2021; 16.07.2021; 12.09.2021<br> Location: Maišiagala, Vilnius District Municipality, Lithuania<br> Spatial resolution: 0.023 m/pixel<br> Number of spectral bands: 204<br> Spectral range: 426-958 nm (visible-near infrared spectrum)<br> Spectral resolution: 2.8 nm<br> Flight altitude: 70 m<br> <br> The dataset consists of blueberry hyperspectral imaging data acquired with a UAV and a BaySpec OCI-F Hyperspectral Imager on several dates. In total, six flights on three different dates were performed. The data from each UAV flight are given as a separate dataset. Each dataset consists of raw and processed hyperspectral imaging data. The raw data include calibration images of white reference and dark background, raw hyperspectral images, and information on the UAV flight path. Calibration data are stored in the folders "...-White", "...-White_FS2", "...-Dark", and "...-Dark_FS2". Raw images are located in subfolders RawImages and RawImages_FS2 of the main data folder, which ends with "..._BI08". The BaySpec Cube Creator 2100 software was used to process raw images into hyperspectral data cubes, which are provided in the format of band sequential image files (BSQ). BSQ files are located in the Cube folders of each dataset together with HDR files containing metadata for each cube. The values of hyperspectral data cubes are in digital numbers, which can be recalculated to reflectance using the reflectance scaling factor. It is specified in the HDR files for each cube individually.</p> <p>Datasheet of the dataset: <a href="https://drive.google.com/file/d/1QV5he5bGazAlN8A5Mpyl1xMc7lQcvy9W">https://drive.google.com/file/d/1QV5he5bGazAlN8A5Mpyl1xMc7lQcvy9W</a><br> <br> Download links:<br> <a href="https://blueberry-dataset.s3.eu-west-1.amazonaws.com/2021-07-07.zip">https://blueberry-dataset.s3.eu-west-1.amazonaws.com/2021-07-07.zip (60.48 GB)</a><br> <a href="https://blueberry-dataset.s3.eu-west-1.amazonaws.com/2021-07-16.zip">https://blueberry-dataset.s3.eu-west-1.amazonaws.com/2021-07-16.zip (177.22 GB)</a><br> <a href="https://blueberry-dataset.s3.eu-west-1.amazonaws.com/2021-09-15.zip">https://blueberry-dataset.s3.eu-west-1.amazonaws.com/2021-09-15.zip (81.12 GB)</a></p>
FlexiGroBots - Blueberry orchard UAV dataset August 2022 - raw data
<p>This data represents the UAV image acquisition from August 2022 in blueberry orchards located in Babe, Serbia.</p> <p>This is the first part of larger dataset, that contains raw images and orthomosaics generated using these images. Raw images are in 100FPLAN, 101FPLAN, and 102PLAN, while generated orthomosaics are in folder Raw orthomosaics August.</p> <p>Another dataset will be uploaded with preprocessed data titled in the similar manner.</p>
FlexiGroBots Ground-level Blueberry Orchard Dataset v1 - RGB Bush Detection Dataset
<p><strong>Ground-level Blueberry Orchard Dataset v1</strong> consists of 2000 RGB images of blueberry orchard scenes captured in the village of Babe, Serbia on three occasions in March, May, and August of 2022. Images are captured using the RGB module of Luxonis OAK-D device, with the resolution of 1920×1080 pixels and stored in the lossless PNG format. </p> <p>The dataset is created for the purpose of training deep learning models for blueberry bush detection, for the task of autonomous UGV guidance. It contains sequences of images captured from the UGV moving and rotating in blueberry orchard rows. Images are captured from a height of approximately 0.5 meters, with the camera angled towards the base of a blueberry plant and the surrounding bank on which it grows. Dataset is captured in real-life outdoor conditions and contains multiple sources of variability (bush shape and size, lighting conditions, shadows, saturation etc.) and artifacts (occlusions by weeds, branches, presence of irregular objects etc.).</p> <p>There are two classes of annotated objects of interest:</p> <ul> <li> <p>Bush, corresponding to the base of the blueberry bush.</p> </li> <li> <p>Pole, corresponding to hail netting poles and similar obstructing objects such as lamp posts or wooden legs of bumblebee hives (distinguishing poles is important to prevent equipment damage in operations such as soil sampling and pruning).</p> </li> </ul> <p>Objects of interest are annotated with bounding boxes. Labels are saved in two formats:</p> <ul> <li> <p>LabelMe JSON format (x1, y1, x2, y2; in pixels)</p> </li> <li> <p>Yolo TXT format (x_center, y_center, width, height; as a ratio of total image size, with numerical labels 0 and 1 corresponding to Bush and Pole)</p> </li> </ul> <p>There are 61 images with no annotated objects, and there are no corresponding label files for these images.</p> <p>The dataset is split into train, validation and test sets with 75%, 10%, and 15% split (1490, 200, and 310 images, respectively). As the data contains sequences of images, the split is made based on sequences rather than individual images to prevent data leakage.</p> <p>Detailed description and statistics are available in:</p> <p>V. Filipović, D. Stefanović, N. Pajević, Ž. Grbović, N. Đurić and M. Panić, "Bush Detection for Vision-based UGV Guidance in Blueberry Orchards: Data Set and Methods," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Vancouver, Canada, 2023. (Accepted)</p>
Landscape structure and farming management interacts to modulate pollination supply and crop production in blueberries
<p><span>Pollination services are affected by landscape context, farming management, and pollinator community structure, all of which impact flower visitation rates, pollen deposition and final production. We studied these processes in Argentina for Highbush Blueberry crops which depend on pollinators to produce marketable yields. </span></p> <p><span>We studied how land cover and honeybee stocking influence the abundance of wild and managed pollinators in blueberry crops, using structural equation modeling to disentangle the cascading effects through which pollinators contribute to blueberry fruit number, size, nutritional content and overall yield. </span></p> <p><span>All pollinator functional groups responded to landscape changes at a spatial scale under 1000 m, and the significance or direction of the effects were modulated by the field-level deployment of honeybee hives. </span></p> <p><span>Fruit diameter increased with pollen deposited, but decreased with honeybee abundance, which, had indirect effects on fruit acidity and sugar content. Honeybees had a positive effect on the number of fruit produced by the plants and also benefited the overall yield (kg plant</span><sup><span>-1</span></sup><span>) through independent effects on both the quality and quantity components of fruit production.</span></p> <p><span><em>Synthesis and applications:</em> </span></p> <p><span>Deployment of beehives in blueberry fields can buffer, but not compensate for the negative effects on honeybee abundance produced by surrounding large scale none-flowering crops. Such compensation would require high-quality beehives by monitoring their health and strength.</span> <span>The </span><span>contribution of honeybees to crop production is not equal across production metrics. That is, higher abundance of honeybees increases the number of berries produced, but at the cost of smaller and more acidic fruits, potentially reducing market value. Growers must consider this trade-off between fruit quantity and quality when actively managing honeybee abundance. </span></p>
Dasineura oxycoccana, the blueberry gall midge distribution, and cultivation areas of blueberry in South Korea
<p>Distribution data of Dasineura oxycoccana for species distribution modeling and cultivation areas of blueberry in South Korea</p> <p> </p>
CRISP Fruit-Picking Teleoperated Dataset: BlueBerry
<p>Dataset of Teleoperated Demonstrations for fruit-picking tasks involving a teleoperated dexterous hand.</p> <p> </p> <p>Data involve the following modalities:</p> <p>- RGB</p> <p>- Tactile</p> <p>- Kinematic</p>
Limited effect of thermal pruning on wild blueberry crop and its root-associated microbiota - Agricultural dataset
<p>These datasets contain all the agricultural data (soil chemistry, blueberry performance, weeds and diseases...) used in our study.</p>
Fig. 2 in Examining the relationship between flower thrips (Thysanoptera: Thripidae) spatial distribution and blueberry (Ericales: Ericaceae) flower density
Fig. 2. Graph of percentage of open blueberry flowers vs. log10 thrips per trap from the Windsor farm on 18 Mar 2010. The black line represents a regression line fitted by least squares regression.
Fig. 1 in Examining the relationship between flower thrips (Thysanoptera: Thripidae) spatial distribution and blueberry (Ericales: Ericaceae) flower density
Fig. 1. Graphs showing percentage of open blueberry flowers vs. thrips per trap from the Inverness farm on a) 30 Jan and b) 5 Feb 2009. The black lines represent regression lines fitted by Theil regression.
Fig. 8 in New report of Brevipalpus yothersi (Prostigmata: Tenuipalpidae) on blueberry in Florida
Fig. 8. Blueberry leaves infested with Brevipalpus yothersi and the bacterial plant disease Xylella sp. (Bacteria: Xanthomonadales).
Fig. 4 in New report of Brevipalpus yothersi (Prostigmata: Tenuipalpidae) on blueberry in Florida
Fig. 4. Brevipalpus yothersi female - ventral view of propodosoma (a), the region between coxal fields of legs III and IV, and the bases of setae 3a and 4a, and the area between setae 3a and 4a, and the area posterior to the 4a setae (b).
Fig. 1 in New report of Brevipalpus yothersi (Prostigmata: Tenuipalpidae) on blueberry in Florida
Fig. 1. Brevipalpus yothersi on blueberry (abundance) leaf - adult (a), egg (b), larva (c), protonymph (d).
Fig. 1. Sample completeness curve for a in Abundance and diversity of beneficial and pest arthropods in buckwheat on blueberry and vegetable farms in north Florida
Fig. 1. Sample completeness curve for a vegetable farm (dark shaded area, 38 samples) and blueberry farm (light shaded area, 19 samples) in Suwannee County, Florida. The solid line is the interpolated insect family-level richness and the dashed line is the extrapolated richness. The shaded areas represent 95% confidence intervals. Sampling effort is standardized based on the sample completeness (the proportion of sampled families to predicted families) at each sampling level, with the circle representing final sampling completeness of 38 samples taken at the vegetable farm and the triangle representing final sampling completeness of 19 samples taken at the blueberry farm.
Fig. 1 in Comparison of attractants, insecticides, and mass trapping for managing Drosophila suzukii (Diptera: Drosophilidae) in blueberries
Fig. 1. The laboratory assay conducted in a wind chamber testing the effectiveness of baits to attract Drosophila suzukii.
Fig. 3 in Comparison of attractants, insecticides, and mass trapping for managing Drosophila suzukii (Diptera: Drosophilidae) in blueberries
Fig. 3. Mean (± SE) number of adult Drosophila suzukii captured in baited traps suspended in a wind chamber. Treatments with the same letter are not significantly different (P> 0.05).
Fig. 6 in Comparison of attractants, insecticides, and mass trapping for managing Drosophila suzukii (Diptera: Drosophilidae) in blueberries
Fig. 6. Mean (± SE) number of female Drosophila suzukii captured in yeast + sugar traps placed in a blueberry field in Hawthorne, Florida, USA, blocked into 4 separate treatments: border spray, mass trapping, alternative row spray, and an untreated control. Populations were monitored weekly during a 6-wk period; asterisks indicate those treatments that were significantly different (P ≤ 0.05) during a sample period.
Fig. 8 in Comparison of attractants, insecticides, and mass trapping for managing Drosophila suzukii (Diptera: Drosophilidae) in blueberries
Fig. 8. Mean (± SE) number of Drosophila suzukii reared from blueberries collected from a field in Hawthorne, Florida, USA, blocked into 4 separate treatments: border spray, mass trapping, alternative row spray, and an untreated control. Fruit was collected weekly for 6 wk. Treatments were not significantly different (P> 0.05).
Fig. 5 in Comparison of attractants, insecticides, and mass trapping for managing Drosophila suzukii (Diptera: Drosophilidae) in blueberries
Fig. 5. Mean (± SE) number of adult Drosophila suzukii captured in yeast + sugar traps placed in a blueberry field in Hawthorne, Florida, USA, blocked into 4 separate treatments: border spray, mass trapping, alternative row spray, and an untreated control. Populations monitored weekly during a 6-wk period; asterisks indicate those treatments that were significantly different (P ≤ 0.05) during a sample period.
Fig. 1 in Discovery of Scirtothrips dorsalis (Thysanoptera: Thripidae) in blueberry fields of Michoacan, Mexico
Fig. 1. Leaves of blueberries colonized by Scirtothrips dorsalis in the field (tunnel farming): (a) plant seriously damaged by thrips, (b) adult thrips feeding on blueberry leaves, (c) egg-eclosion from the epidermis of a blueberry leaf.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.