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208 results for “Blueberry”
Hyperspectral Unmixing Dataset of UAV Gathered Blueberry Field Data
<p>Hyperspectral Unmixing dataset created from hyperspectral data gathered usign SPECIM push-broom hyperspectral camera mounted on a UAV flying over blueberry fields in Lithuania. Created dataset contains six data classes and linear mixtures from raw data. All data is given in Python Numpy array .npy files. </p> <p>To keep the annonimity of data owners only the non georectified data cubes are published.</p> <p>Dataset includes three hyperpectral data cubes of blueberry fields and Dark reference cube to show camera noise.</p> <p><strong>Data structure:</strong></p> <p>cube_1, cube_2, cube_2 and Dark - folder with hyperspectral data.</p> <p>calibration_data.npy - Data of calibration plates (with 40%, 10% and 5% reflectance values) from hyperspectral flight that were used to conver DN to reflectance.</p> <p>endmembers.npy - Spectra of siz endmembers (classes) used in the dataset.</p> <p><strong>cube_x folders include:</strong></p> <p>class_matrix.npy - Numpy matrix file of hyperspectral image classes (classification results)</p> <p>raw_data.npy - Hyperspectral cube created from raw camera data (with DN values)</p> <p>data_cube_3_0.npy and abundances_3_0.npy - Classified and mixed (using slidin window of 3x3 pixels with no overlap) hyperspectral data cube and class abundance matrix. </p> <p>endmember_errors.npy - matrix of variation for each of endmembers in the hyperspectral cube (used for evaluation mostly.)</p> <p><strong>Dark folder:</strong></p> <p>includes data folder with raw-dark_fl1_20230830_140006_radiance.dat and .hdr ENVI raster data files (library like <em>rasterio</em> for Python can used to read these files). This is the dark (0% reflectance) data cube and header file used in calibration.</p>
Linked collectors and determiners for: Vaccinium carmesinum (Ericaceae), a new species of blueberry from Mt. Tago Range, Mindanao Island, Philippines.
Natural history specimen data linked to collectors and determiners held within, "Vaccinium carmesinum (Ericaceae), a new species of blueberry from Mt. Tago Range, Mindanao Island, Philippines". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/c8c6b23b-ad69-4b91-89d3-057fd141c4ef">https://bionomia.net/dataset/c8c6b23b-ad69-4b91-89d3-057fd141c4ef</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/c8c6b23b-ad69-4b91-89d3-057fd141c4ef">https://gbif.org/dataset/c8c6b23b-ad69-4b91-89d3-057fd141c4ef</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Wild Bee Specimens from Blueberry and Raspberry Farms in the Champlain Valley, Vermont, USA.
Natural history specimen data linked to collectors and determiners held within, "Wild Bee Specimens from Blueberry and Raspberry Farms in the Champlain Valley, Vermont, USA". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/26c7440a-f4e4-4564-9901-9aac6e116536">https://bionomia.net/dataset/26c7440a-f4e4-4564-9901-9aac6e116536</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/26c7440a-f4e4-4564-9901-9aac6e116536">https://gbif.org/dataset/26c7440a-f4e4-4564-9901-9aac6e116536</a>. Formatted as a Frictionless Data package.
BlueberryDCM: A Canopy Image Dataset for Detection, Counting, and Maturity Assessment of Blueberries
<p>The <strong>BlueberryDCM</strong> dataset consists of <strong>140 RGB images</strong> of blueberry canopies captured at varied spatial scales. All the images were acquired using smartphones in natural field light conditions in different orchards in the season of 2022, with 134 images in Mississippi and 6 images in Michigan. A total of <strong>17,955 bounding box annotations</strong> were manually done in the <a href="https://www.robots.ox.ac.uk/~vgg/software/via/">VGG Image Annotator</a> (VIA) (v2.0.12) for the blueberry instances of two fruit maturity classes, "<strong>Blue</strong>" and "<strong>Unblue</strong>", representing ripe and unripe fruit, respectively. In addition, for each maturity class, there are two sub-categories in the annotation, "<strong>visible</strong>", and "<strong>occluded</strong>", to indicate whether the fruit is fully visible in the canopy or partially occluded. The original annotation format exported from the VGG is <a href="https://www.robots.ox.ac.uk/~vgg/software/via/">VIA .json.</a> The derived annotation files in two other formats, .xml (<a href="https://docs.cvat.ai/docs/manual/advanced/formats/format-voc/">Pascal VOC format</a>) and .txt (<a href="https://docs.ultralytics.com/datasets/detect/#ultralytics-yolo-format">YOLO format with noralized xywh</a>, with 0, 1, 2, and 3 denoting the four categories of "<strong>Unblue_visible</strong>", "<strong>Unblue_occluded</strong>", "<strong>Blue_visible</strong>", and "<strong>Blue_occluded</strong>" bluerries, respectively) are provided in the dataset for the compatibility of a wide range of object detectors. Hence, the dataset contains both the raw images (.jpg) and three corresponding annotations files (.json, .xml, and .txt) with the same file names, totaling about 107 MB in file size. </p> <p> </p> <p>The dataset was used for in a study (see below) on the <a href="https://www.sciencedirect.com/science/article/pii/S2772375524002259">evaluation of YOLOv8 and YOLOv9 models for blueberry detection, counting, and maturity assessment</a>. The detection accuracy of 93% mAP@50 was achieved by YOLOv8l, with an error of about 10 blueberries in fruit counting and an error of 3.6% in estimating the "Blue" fruit percentage. Software programs for the modeling work are made publicly available at: <a href="https://github.com/vicdxxx/BlueberryDetectionAndCounting">https://github.com/vicdxxx/BlueberryDetectionAndCounting</a>. In addition, the blueberry dataset was also used as a preliminary database for developing an iOS-based mobile application, which is described in <a href="https://doi.org/10.13031/aim.202401022">Deng, B., Lu, Y., WanderWeide, J., 2024. Development and preliminary evaluation of a deep learning-based fruit counting mobile application for highbush Blueberries. 2024 ASABE Annual International Meeting 2401022</a></p> <p> </p> <p>Details about the dataset curation and statistics as well as modeling experiments are described in the journal article: <a href="https://www.sciencedirect.com/science/article/pii/S2772375524002259">Deng, B., Lu, Y., 2024</a>. <a href="https://doi.org/10.1016/j.atech.2024.100620">Detection, Counting, and Maturity Assessment of Blueberries in Canopy Images using YOLOv8 and YOLOv9</a><a href="https://www.sciencedirect.com/science/article/pii/S2772375524002259">. Smart Agricultural Technology.</a> <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.atech.2024.100620" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.atech.2024.100620</a>. If you use the dataset in published research, please consider citing the dataset or the journal article. Hopefully, you find the dataset useful. </p>
Figure 1 in First record, current status, symptoms, infested cultivars and potential impact of the blueberry bud mite, Acalitus vaccinii (Keifer) (Prostigmata: Eriophyidae) in South Africa
Figure 1 Acalitus vaccinii (Keifer, 1939) in South Africa: A – colony at the base of a symptomatic flower bud bract of Vaccinium corymbosum 'Berkeley'; B – enlarged part of the colony shown in Figure 1A; C – relatively small colony between corolla and calyx ofV. corymbosum 'Elliott' flower with callus-like tissue caused by the mites. Symptoms caused byA. vaccinii in South Africa: D – flower galls on V. virgatum 'Centurion' which are more compact than those on V. corymbosum 'Berkeley'in Figure 1E; E – rosette-like flower galls on V. corymbosum 'Berkeley'; F – hypertrophic red "roughened" callus-like tissue of a flower gall on V. corymbosum 'Ivanhoe'; G – red callus-like tissue on outside of corolla ofV. corymbosum 'Elliott' flower.
Landscape structure and farming management interacts to modulate pollination supply and crop production in blueberries
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A first complete phylogenomic hypothesis for diploid blueberries (Vaccinium section Cyanococcus)
<p><strong>The premise of the study: </strong>The true blueberries, (<em>Vaccinium</em> sect. <em>Cyanococcus</em>; Ericaceae), endemic to North America, have been intensively studied for over a century. However, with species estimates ranging from 9 to 24 and much confusion regarding species boundaries, this ecologically and economically valuable group remains inadequately understood at a basic evolutionary and taxonomic level. As a first step toward understanding the evolutionary history and taxonomy of this species complex, we present the first phylogenomic hypothesis of the known diploid blueberries.</p> <p><strong>Methods</strong>: We used flow cytometry to verify the ploidy of putative diploid taxa and a target-enrichment approach to obtain a genomic dataset for phylogenetic analyses.</p> <p><strong>Results</strong>: Despite evidence of gene flow, we found that a primary phylogenetic signal is present. Monophyly for all morphospecies was recovered, with two notable exceptions: one sample of <em>V. boreale</em> was consistently nested in the V<em>. myrtilloides</em> clade, and <em>V. caesariense</em> was nested in the <em>V. fuscatum</em> clade. One diploid taxon, <em>Vaccinium pallidum</em>, is implicated as having a homoploid hybrid origin.</p> <p><strong>Conclusions</strong>: This foundational study represents the first attempt to elucidate evolutionary relationships of the true blueberries of North America with a phylogenomic approach and sets the stage for multiple avenues of future study such as a taxonomic revision of the group, the verification of a homoploid hybrid taxon, and the study of polyploid lineages within the context of a diploid phylogeny.</p>
Classification and identification of pinecones mulching on blueberry cultivation based on crop leaf characteristics and hyperspectral data
<p><span>Supplementary Figure S1: Spectra preprocessing before and after.; Table S1: The evaluation results of the classification model of leaf growth and physiology.; Table S2: The evaluation results of the classification model of VIs.; Table S3: The evaluation results of the classification model of VNIR.; Table S4: The evaluation results of the classification model of SWIR.</span></p>
HyperspectralBlueberries: a dataset of hyperspectral reflectance images of normal and defective blueberries
<p>The <strong>HyperspectralBluberries</strong> dataset consists of hyperspectral datacubes, which were acquired by an in-house assembled benchtop line scanning system, from 420 blueberries of two categories, including 210 sound fruit and 210 samples with various defects. The fruit samples were hand-picked from a commercial orchard. Each scanning event, which was done for an array of 42 samples, yields two files in image formats .bil (band-interleaved-by-line) and .hdr (header), which store the hyperspectral raw data and associated metadata, respectively, and are both necessary for loading hyperspectral data for processing. In addition to sample scanning, a white reference was also scanned, which can be used for standardizing spectral responses. As a result, there are 22 files in the dataset, totaling about 25 GB in file size. The sample file names are descriptive, indicating the blueberry category and number information. The dataset was used for developing machine learning models for differentiating between normal and defective blueberries, achieving an overall accuracy of 96.6%. Software programs for the modeling work are publicly available at: <a href="https://github.com/vicdxxx/Blueberry-Defect-Detection-by-Hyperspectral-Imaging">https://github.com/vicdxxx/Blueberry-Defect-Detection-by-Hyperspectral-Imaging.</a></p> <p>Details about the dataset curation and modeling experiments are described in the journal article: <a href="https://www.sciencedirect.com/science/article/pii/S2772375524000789">Deng, B., Lu, Y., Stafne, E. (2024). </a><a href="https://www.sciencedirect.com/science/article/pii/S2772375524000789">Fusing Spectral and Spatial Features of Hyperspectral Reflectance Imagery for Differentiating between Normal and Defective Blueberries. Smart Agricultural Technology</a>. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.atech.2024.100473" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.atech.2024.100473</a>. If you use the dataset in published research, please consider citing the dataset or the <a href="https://doi.org/10.1016/j.ecoinf.2024.102546">journal article</a>. Hopefully, you find the dataset useful. </p>
Fig. 2 in New report of Brevipalpus yothersi (Prostigmata: Tenuipalpidae) on blueberry in Florida
Fig. 2. Brevipalpus yothersi female - dorsal propodosoma (a), opisthosoma (b).
Fig. 7 in New report of Brevipalpus yothersi (Prostigmata: Tenuipalpidae) on blueberry in Florida
Fig. 7. Ventral view of Brevipalpus yothersi male (a) and Brevipalpus yothersi larva (b).
Fig. 2 in Comparison of attractants, insecticides, and mass trapping for managing Drosophila suzukii (Diptera: Drosophilidae) in blueberries
Fig. 2. Commercially available trap from RIGA® AG used in mass trapping.
Dataset _ Multi-scale factors in blueberry pollination
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Effect of Blueberry Supplementation on Alzheimer's Biomarkers
ClinicalTrials.gov study NCT05172128. IPD Sharing: NO. Countries: 1. Publications: 4.
An Investigation Into the Effects of a Wild Blueberry Powder and a Wild Blueberry Extract on Cognition in Older Adults
ClinicalTrials.gov study NCT02446314. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Short-term Health Effects of Wild Blueberry Juice Consumption
ClinicalTrials.gov study NCT02139878. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Blueberry Consumption and Type 2 Diabetes
ClinicalTrials.gov study NCT02972996. IPD Sharing: NO. Countries: 1. Publications: 1.
Evaluating the Availability of Berry Phytonutrients Post-consumption of Fresh and Processed Blueberry by Healthy Adults
ClinicalTrials.gov study NCT04175106. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Blueberries, Inflammation, Motivation, and Physical Activity
ClinicalTrials.gov study NCT05735587. IPD Sharing: YES. Countries: 1. Publications: 1.
A first complete phylogenomic hypothesis for diploid blueberries (Vaccinium section Cyanococcus)
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