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6 results for “Root image analysis”
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>
Analysis of the root diameter distribution from time series images of real and simulated Cassava root systems
<p>The data was collected, simulated and analyzed in the framework of the CassavaStore project (a collaboration between IBG-2, Forschungszentrum Jülich, Germany and different institution from Thailand; for details see <a href="https://www.international-bioeconomy.org/cassavastore_eng">https://www.international-bioeconomy.org/cassavastore_eng</a>). Aim of this project is to get a better understanding of storage root development in cassava (<em>Manihot esculenta</em> Crantz) in order to optimize cassava growth with respect to variety breeding and growth management. The storage root is one of the main providers of starch in Thailand and therefore of high economic importance. Monitoring the formation of storage roots over time via quantification of the root diameter distribution of excavated root systems was one of the key aspects addressed in this project. To measure the diameters a software was developed that identifies roots in RGB images and analyzes the diameters along each identified root automatically. The published data contains 1) analyzed images from cassava roots that were acquired in a video box; 2) simulated virtual root model images with known root diameter distributions that were used to validate the analysis approach; 3) a description of the data and the folder structure.</p>
Images, data, and statistical analysis scripts for review article on cover crop roots
<p>Images, data, and statistical analysis scripts for review article on cover crop roots.</p> <blockquote> <p><strong>Optimization of root traits to provide enhanced ecosystem services in agricultural systems: a focus on cover crops</strong> - [<a href="https://doi.org/10.1111/pce.14247">https://doi.org/10.1111/pce.14247</a>]</p> </blockquote> <ul> <li>Research site, planting, and growth <ul> <li>10/2020 - 04/26/2021 cover crop field trial. DDPSC FRS at Planthaven Farm, O'Fallon, MO 63366 (latitude 38.848240°, longitude -90.686640°). </li> <li>The field was tilled before sowing of cover crops. Seed for each cover crop were spread in using a push seed spreader and were lightly irrigated.</li> <li>Alfalfa (<em>Medicago sativa</em>), dundale pea (<em>Pisum sativum</em>), milkvetch (<em>Astragalus canadensis</em>, <em>Astragalus bisulcatus</em>), crimson clover (<em>Trifolium incarnatum</em>), hairy vetch (<em>Vicia villosa</em>), mustard (<em>Brassica junce</em>a var Mighty Mustard, var Kodiak), barley (<em>Hordeum vulgare</em>), wheat (<em>Triticum aestivum</em>, winter, spring), winter rye (<em>Secale cereale</em>), and triticale (× T<em>riticosecale</em> Wittmack).</li> </ul> </li> <li> <p>Field harvest measurements</p> <ul> <li> <p>Four canopy images were taken across each cover crop row using a Canon 5DS R camera. Images were taken from above each plot at 5ft height manually. Green color was thresholded from the canopy images in batch using OpenCV python script and the percent green cover calculated (Jupiter notebook).</p> </li> <li> <p>Five soil monoliths were excavated using a "shovelomics" approach with an average monolith size of 25.4cm x 25.4cm x 20 cm. The remaining four soil monoliths were destructively analyzed.</p> </li> <li> <p>One soil monolith was imaged using a Canon 50D DLSR camera in a photogrammetry shed. All photogrammetric analysis was conducted using Pix4D mapper software (Pix4D S.A. Prilly, Switzerland), and point cloud cleaning was conducted in CloudCompare V2. 10.2.</p> </li> <li> <p>Cover crop shoots from the remaining soil monoliths were cut and placed into a paper bag for dry biomass determination (60oC for 5 days). A cover crop shoot count was conducted for each monolith with each tiller considered as a shoot for the grasses (barley, wheat, triticale). After cover crop shoot harvesting, a photo was then taken of each soil monolith with remaining weed biomass. A weed score was assigned to each image by one trained researcher with a score 1 low weeds to 5 high weed presence.</p> </li> <li> <p>Soil monoliths were the soaked briefly in water and then the soil washed using a hose keeping the roots. Roots were then scanned on an Epson Expression 12000XL Photo Scanner with transparency unit. Images labeled with "_part" were samples with too many roots for scanning and so were separately weighed. Dry root biomass was taken for the scanned and unscanned roots separately. Root length was determined from images using software RhizoVision Explorer (https://doi.org/10.5281/zenodo.4095629), total root length was estimated using scanned root length and scanned dry biomass with unscanned root biomass.</p> </li> <li> <p>Along each cover crop plot a 10ft trench was dug using a Yanmar Excavator Vi020-6 perpendicular to the row with each trench fully bisecting the plot. Trench was one bucket wide (19 inches) and approximately 36 inches deep in the middle of the row. The five deepest roots that could be observed in the trench wall was measured manually with a tape measure for each cover crop. A garden trowel and shovel were used to excavate and confirm roots in trench wall.</p> </li> <li> <p>Data was analyzed using R Statistics script and raw data used for data processing and figure generation (2021PlantHavenCovercrop_dataprocessing.R). PCA analysis was conducted using the “FactoMineR” package (Husson <em>et al</em>. 2019) to explore the relationships between the traits within the dataset and clustered by family.</p> </li> </ul> </li> </ul> <p>Individual ZIP file contents:</p> <ul> <li><code><strong>2021PlantHavenCovercrop_CanopyImages.zip</strong></code> – Raw canopy images, processed percent green cover images, and Jupiter notebook python script (2021PlantHavenCovercrop_ImageBatchColorThreshold.ipynb).</li> <li><code><strong>2021PlantHavenCovercrop_RootFlatbedImages.zip</strong></code> – Raw flatbed root scans of cover crops and processed images using RhizoVision Explorer.</li> <li><code><strong>2021PlantHavenCovercrop_SoilMonolithWeedImages.zip</strong></code> – Images of soil monoliths after cover crop shoot biomass was removed.</li> <li><code><strong>2021PlantHavenCovercrop_dataprocessing.zip</strong></code> – R Statistics script and raw data used for data processing and figure generation (2021PlantHavenCovercrop_dataprocessing.R).</li> <li><code><strong>2021PlantHavenCovercrop_ShootPhotogrammetry.zip</strong></code> – 3D models of cover crop shoots from excavated soil monoliths. The .bin files can be opened using CloudCompare app.</li> </ul> <p> </p> <p> </p>
Root images for testing concatenation and statistical analysis with RhizoVision Explorer
<p>Thresholded images of cleaned and scanned roots as described for a paper entitled:</p> <p>"Divide and conquer: Using RhizoVision Explorer to aggregate data from multiple root scans using image concatenation and statistical methods"</p> <p>The zip file contains two main folders for Original and Concatenated images. Each contains a subfolder for the 3 different image sets for switchgrass, poplar, and petaland (which also includes some arctic samples). Original images include multiple scans of the same sample where the sample is denoted as a letter. Concatenated images were generated in python combining multiple scans of the same sample into one large image based on the common letter.</p>
Images and statistical analysis of alfalfa root crowns from inside and outside disease rings caused by cotton root rot
<p>This repository contains raw image data of root crowns imaged using the backlit RhizoVision Crown platform of alfalfa plants from either inside or outside disease rings caused by cotton root rot for a manuscript to be submitted. Data files and the R scripts are included for complete statistical analysis associated with the imaged root crown set.</p> <p>Please cite both this repository and below journal article if reusing this data for a publication.</p> <p><strong>Manuscript Title:</strong> Digital imaging to evaluate root system architectural changes associated with soil biotic factors</p> <p><strong>Authors: </strong>Chakradhar Mattupalli, Anand Seethepalli, Larry M. York, Carolyn A. Young</p> <p><strong>Journal Article: </strong><a href="https://doi.org/10.1094/PBIOMES-12-18-0062-R">https://doi.org/10.1094/PBIOMES-12-18-0062-R</a> (open access)</p> <p><strong>Image Files</strong></p> <p>afalfa_roots_raw_images.zip - 264 images in PNG format directly from a monochrome camera with gamma at 3.9 to make near-segmented raw images in greyscale</p> <p>alfalfa_feature_images.zip - 264 images in PNG format with a subset of computed features overlaid</p> <p>alfalfa_segmented_images.zip - 264 black and white, binary images in PNG format that result from simple thresholding of the raw images</p> <p>I_scale_1.png - an image of a 6 inch ruler for extracting pixel to physical unit conversion</p> <p>metadata.csv - metadata output from RhizoVision Analyzer with diameter ranges and other options used for image analysis</p> <p><strong>Statistical Analysis</strong></p> <p>alfalfaCRR_features_10012018.csv - extracted features from RhizoVision Analyzer v1.0.3 - called by name in R script</p> <p>RootArchitectureFieldStudyimagetoIDmap.csv - mapping of image file names to plot identity - called by name in R script</p> <p>manuscript complete root rot RVC analysis.R - R script with all analysis that used the data from the imaged root crowns</p> <p> </p> <p> </p>
Root crown images of soybean and wheat and statistical analysis for RhizoVision Crown
<p>This repository contains raw image data of root crowns imaged using the backlit RhizoVision Crown platform of soybean and wheat plants phenotyped in Missouri and Oklahoma, respectively, as described in the below manuscript. Data files and the R scripts are included for complete statistical analysis associated with the imaged root crown set as well as validation using images of copper wires and simulated root images. We request any reuse of this data to both cite this repository and also the publication given below.</p> <p><strong>Citation:</strong></p> <p>Seethepalli, A., Guo, H., Liu, X., Griffiths. M. G., Almtarfi, H., Li, Z., Liu, S., Zare, A., Fritschi, F., Blancaflor, E., Ma, X., and York, L. M. (2020). RhizoVision Crown: An integrated hardware and software platform for root crown phenotyping. <em>Plant Phenomics. doi:10.34133/2020/3074916</em></p> <p><strong>Link: </strong><a href="https://spj.sciencemag.org/plantphenomics/2020/3074916/">https://spj.sciencemag.org/plantphenomics/2020/3074916/</a></p> <p>Root crown images were acquired using the RhizoVision Imager software available at:</p> <p><a href="https://zenodo.org/record/2585882#.XWgOAeNKiUk">https://zenodo.org/record/2585882#.XWgOAeNKiUk</a></p> <p>Image analysis of physical wires, simulated dicot and monocot root systems, and soybean and wheat root crowns was originally conducted with RhizoVision Analyzer. However, Analyzer has been replaced by RhizoVision Explorer that uses the same underlying algorithms and the analysis can be recreated using the 'whole root' mode of Explorer, available here:</p> <p><a href="https://zenodo.org/record/4095629">https://zenodo.org/record/4095629</a></p> <p><strong>Image Files</strong></p> <p>physical_validation_wires.zip - A set for validation of physical units using 10 images of 2 individual wires of 5 gauges (10, 16, 22, 28, 32) in PNG format acquired using the RhizoVision Crown hardware platform. Includes a CSV file containing measured length and diameters as well as those from image analysis using RhizoVision Analyzer.</p> <p>Soybean_RVC_RootCrowns.zip - 2,778 greyscale images in PNG format of field excavated soybean root crowns.</p> <p>Wheat_RVC_RootCrowns.zip - 1,754 greyscale images in PNG format of field excavated wheat root crowns.</p> <p>metadata_Analyzer_allImageSets.zip - CSV files with the metadata files output by the RhizoVision Analyzer software for the physical validation, validation of simulated dicots and monocots (described in preprint), and the soybean and wheat root crown images. Useful to see thresholding and physical resolution settings.</p> <p>The simulated monocot and dicot root systems images and associated data are described in the preprint and available from Lobet et al. here:</p> <p>Lobet, Guillaume, Koevoets, Iko, Noll, Manuel, Tocquin, Pierre, Meyer, Patrick E, Pagès, Loic, & Périlleux, Claire. (2016). Library of simulated root images, with different noise levels [Data set]. Zenodo.</p> <p><a href="https://zenodo.org/record/208214">https://zenodo.org/record/208214</a></p> <p><strong>Statistical Analysis</strong></p> <p>Rcode_allData.zip - Contains a single .R text file with code to reproduce all statistics and data figures. Contains several .CSV data files used by the R code. Assumption for running is that R code and data files are in the same working directory and setwd() is set there. Also, libraries needed are called at the top of the script and need to be previously installed.</p> <p><strong>Hardware Plans</strong></p> <p>RhizoVision_Crown_Hardware_Plans_Details.pdf - For completeness, hardware plans for the RhizoVision Crown platform as described in the manuscript are included. Currently, we recommend the following camera and lens to use in this system:</p> <p>Basler 5472 monochrome usb camera</p> <p><a href="https://graftek.biz/products/basler-aca5472-17um">https://graftek.biz/products/basler-aca5472-17um</a></p> <p>This Moritex 25 mm lens:</p> <p><a href="https://graftek.biz/products/moritex-ml-u2515sr-18c?">https://graftek.biz/products/moritex-ml-u2515sr-18c?</a></p> <p><br> <br> ⇒ Version 2 of this repository includes updates based on peer-review of the manuscript. New versions of physical_validation_wires.zip and Rcode_allData.zip were uploaded.</p> <p>⇒ Version 3 of this repository includes updates based on peer-review of the manuscript. A new version of Rcode_allData.zip was uploaded.</p>
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