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14 results for “Minirhizotron”
Minirhizotron images for RootPainter demo
<p>This dataset consists of 100 minirhizotron images (2340 x 2400 pixels; resolution: 148 px/mm) taken with the manual UHD minirhizotron camera (<a href="https://www.vienna-scientific.com/products/minirhizotron-systems/manual/">VSI-BARTZ MS-190</a>) from Vienna Scientific Instruments.</p> <p>The images were acquired in a grassland field experiment (<a href="https://gepris.dfg.de/gepris/projekt/420444099?language=en">POEM experiment</a>) in which the order of arrival of three plant functional groups (forbs, grasses and legumes) was manipulated. In each plot, two root observation tubes were installed at a 45° angle six months before the start of the experiment (i.e. the first sowing event on April 13, 2021). Along each tube, images are taken at 18 different depths (from 1.4 to 49.5 cm) twice a month from April to September and once a month from October to March.</p> <p>The POEM experiment is funded by the <a href="https://www.dfg.de/">German Research Foundation</a>.</p>
Automated Minirhizotron Validation Data
<p>This dataset is contains the validation data (raw images and binary masks generated by hand) for the automated minirhizotrons in our paper 'HIGH FREQUENCY ROOT DYNAMICS: SAMPLING AND INTERPRETATION USING REPLICATE ROBOTIC MINIRHIZOTRONS' published in Journal of Experimental Botany. <a href="https://doi.org/10.1093/jxb/erac427">https://doi.org/10.1093/jxb/erac427</a></p> <p>The dataset consists of one readme, four .7z files and one python script all contained in one .7z file.</p> <p>The files are described in the readme file.</p> <p>Some of these data were collected and all processed as part of the Marie Sklodowska-Curie project 748893 'MrPARTS' awarded to Richard Nair. We also acknowledge the generous support of Markus Reichstein at MPI-BGC Jena including to the MaNiP project through the Max Planck research prize 2013</p>
Minirhizotron data from Fertilization Experiment, Hog Island, VA, 1992
Minirhizotron data from fertilization experiment. On 36-year old dune ridge on Hog Island, VA. Data presented are a complete listing of all roots observed during the study period. See Weber, E. 1994. The effect of nitrogen fertilization on the phenology of roots in a barrier island sand dune community. 1994. M.S. Thesis, Old Dominion University for a full description of the data gathering protocol. There were 83 frames (18 mm x 13.5 mm) sampled for every tube. If a frame had no roots in it, then that frame on that date does not appear in this data set.
Dataset for "Root Length Estimation: Automated Minirhizotron Image Analysis with Convolutional Networks without Segmentation"
<p>This data contains 4015 root images, splitted into 4 datasets, acquired using two minirhizotron (MR) system types - manual (Dataset 1 & Dataset 4) and automated (Dataset 2 & Dataset 3). It includes four crop species (corn, pepper, melon, and tomato) grown under various abiotic stresses. The data was acquired by researchers from Ben-Gurion University of the Negev, Beer Sheva, Israel, and used for research of automated TRL estimation with Convolutional Neural Networks.</p> <p>The annotations were conducted manually using the Rootfly software (Wells and Birchfield, Clemson University, South Carolina, USA), and data were transformed as CSV formats. In this software, the annotator must draw a root by marking points along the selected root. These points usually correspond to the coordinates at the start and the end of the root, and curving points along the root. These points are then connected in a line, the length of which reflects the real length of the selected root. The annotations has been done for all roots within an image, and for all images in the provided dataset.</p> <p>The provided annotations include the total root length (TRL) per image (mm) and the coordinates of annotated points.</p> <p>The annotations are given in two types of files:</p> <p>"TRL.csv" files: contain the image names and corresponding TRL values (mm).</p> <p>"pointsOutput.csv" files: contain the annotated image names and the coordinates of the points of the roots in the image (if the image contains roots) in the form of x1, y1, x2, y2, x3, y3, etc. It the image doesn't have roots, the file contains only its name.</p>
Fine root production and mortality from minirhizotrons at the Hubbard Brook Experimental Forest
This data set reports observations of fine root growth, mortality and lifespan in the mature forest. The lifespan data can be combined with fine root biomass data to estimate fine root production in the forests (Fahey et al. 2005). This data set was from reference plots for a snow manipulation experiment and the results are summarized in Tierney et al. (2001). These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Minirhizotron frame information from Fertilization Experiment, Hog Island, VA, 1992
Minirhizotron data from fertilization experiment. On 36-year old dune ridge on Hog Island. This data file lists sampled frames in each of the minirhizotron tubes. The file also lists the section that each frame is associated with. This file is to be used in conjunction with hog.asc in dataset VCR03094.
Minirhizotron Root Data for Hog Island, VA 1992-1998
Minirhizotron data from fertilization experiment. On 36-year old dune ridge on Hog Island from 1992 to 1998. These tubes were sampled twice a year with the fall data from 1992 included. In most cases 26 frames were sampled randomly within specific depth intervals and digitized for each tube. Plot 1 was discontinued after 10/93 and Plot 2 was discontinued after 4/93. In this data set if there were no roots in a frame then no ROOT ID appears and length and width are 0. fertilization treatment = In 1992, three applications of 15 gN / m2 (March, June, October). After 1992, 15 gN / m2 applied once per year (mid-summer) through 1998.
data for publication "Benefits of biobased fertilizers as substitutes for synthetic nitrogen fertilizers: Field assessment combining minirhizotron and UAV-based spectrum sensing technologies"
<p>Dataset for the scientific publication "Benefits of biobased fertilizers as substitutes for synthetic nitrogen fertilizers: Field assessment combining minirhizotron and UAV-based spectrum sensing technologies" in the Journal Frontiers of Environmental Science. </p><p><a href="https://doi.org/10.3389/fenvs.2022.988932">https://doi.org/10.3389/fenvs.2022.988932</a></p>
Minirhizotron images for RootPainter demo
<p>This dataset consists of 100 minirhizotron images (2340 x 2400 pixels; resolution: 148 px/mm) taken with the manual UHD minirhizotron camera (<a href="https://www.vienna-scientific.com/products/minirhizotron-systems/manual/">VSI-BARTZ MS-190</a>) from Vienna Scientific Instruments.</p> <p>The images were acquired in a grassland field experiment (<a href="https://gepris.dfg.de/gepris/projekt/420444099?language=en">POEM experiment</a>) in which the order of arrival of three plant functional groups (forbs, grasses and legumes) was manipulated. In each plot, two root observation tubes were installed at a 45° angle six months before the start of the experiment (i.e. the first sowing event on April 13, 2021). Along each tube, images are taken at 18 different depths (from 1.4 to 49.5 cm) twice a month from April to September and once a month from October to March.</p> <p>The POEM experiment is funded by the <a href="https://www.dfg.de/">German Research Foundation</a>.</p>
Data from: Root volume distribution of maturing perennial grasses revealed by correcting for minirhizotron surface effects
Aims: Root architecture drives plant ecology and physiology, but current detection methods limit understanding of root placement within soil profiles. We developed a statistical model of root volume along depth gradients and used it to infer carbon storage potential of land-use changes from conventional agriculture to perennial bioenergy grasses. Methods: We estimated root volume of maize-soybean rotation and three perennial grass systems (Miscanthus × giganteus, Panicum virgatum, tallgrass prairie mix) by Bayesian modeling from minirhizotron images, correcting for small images and near-surface underdetection. We monitored seasonal and inter-annual changes in root volume distribution, then validated our estimates against root mass from core samples. Results: The model explained 29% of root volume variation and validated well against core mass. Seventh-year perennials had greater belowground biomass than maize-soybean both in total (11-16×) and throughout the profile (2-17× at every depth < 120 cm). Perennials' relative depth allocations were stable over time, while total root volume increased through five years. In 2012 a historically hot, dry summer damaged maize while perennials appeared resilient, suggesting their large-deep root systems aid drought resistance. Conclusions: Perennial root systems are large, deep, and persistent. Converting row crops to perennial bioenergy grasses likely sequesters carbon in a large, potentially very stable, soil pool.
PRMI: A dataset of minirhizotron images for diverse plant root study
<p>Understanding a plant's root system architecture (RSA) is crucial for a variety of plant science problem domains including sustainability and climate adaptation. Minirhizotron (MR) technology is a widely-used approach for phenotyping RSA non-destructively by capturing root imagery over time. Precisely segmenting roots from the soil in MR imagery is a critical step in studying RSA features. In this paper, we introduce a large-scale dataset of plant root images captured by MR technology. In total, there are over 72K RGB root images across six different species including cotton, papaya, peanut, sesame, sunflower, and switchgrass in the dataset. The images span a variety of conditions including varied root age, root structures, soil types, and depths under the soil surface. All of the images have been annotated with weak image-level labels indicating whether each image contains roots or not. The image-level labels can be used to support weakly supervised learning in plant root segmentation tasks. In addition, 63K images have been manually annotated to generate pixel-level binary masks indicating whether each pixel corresponds to root or not. These pixel-level binary masks can be used as ground truth for supervised learning in semantic segmentation tasks. By introducing this dataset, we aim to facilitate the automatic segmentation of roots and the research of RSA with deep learning and other image analysis algorithms.</p>
PRMI: A dataset of minirhizotron images for diverse plant root study
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Data from: Root volume distribution of maturing perennial grasses revealed by correcting for minirhizotron surface effects
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Images of minirhizotron 1 and 2 during 2022 and 2023 in Schifflange (Luxembourg) and codes for data analysis
<p>Repository containing the minirhizotron images for MR tube 1 and 2 (MR1.zip and MR2.zip) and containing the codes for the root properties extrapolation and data analysis (minirhizotron_experiment.zip).</p>
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