Ag Data Commons
Browse
ARCHIVE
GH3 DP90 Keyence 46_101 JPEG.zip (267.53 MB)
ARCHIVE
GH3 DP90 Keyence 1_45 JPEG.zip (191.34 MB)
ARCHIVE
GH3 DP90 Keyence 102_125 JPEG.zip (114.06 MB)
ARCHIVE
Gb cv Phytogen 800.zip (52.2 MB)
ARCHIVE
Gb cv Pima 379.zip (56.4 MB)
ARCHIVE
Gb cv Pima S7.zip (108.03 MB)
ARCHIVE
Gh cv Coker 312.zip (117.14 MB)
ARCHIVE
Gh cv Deltapine 90.zip (58.06 MB)
ARCHIVE
Gh cv Half and Half.zip (53 MB)
ARCHIVE
model_weights.zip (146.87 MB)
TEXT
manual_annotations.coco_.json (1.31 MB)
TEXT
ai_assisted_annotations.coco_.json (603.5 kB)
1/0
12 files

Data from: Efficient imaging and computer vision detection of two cell shapes in young cotton fibers

dataset
posted on 2024-02-21, 19:22 authored by Benjamin P. Graham, Jeremy Park, Grant BillingsGrant Billings, Amanda M. Hulse-Kemp, Candace H. Haigler, Edgar Lobaton

Methods

Cotton plants were grown in a well-controlled greenhouse in the NC State Phytotron as described previously (Pierce et al, 2019). Flowers were tagged on the day of anthesis and harvested three days post anthesis (3 DPA). The distinct fiber shapes had already formed by 2 DPA (Stiff and Haigler, 2016; Graham and Haigler, 2021), and fibers were still relatively short at 3 DPA, which facilitated the visualization of multiple fiber tips in one image.

Cotton fiber sample preparation, digital image collection, and image analysis:

Ovules with attached fiber were fixed in the greenhouse. The fixative previously used (Histochoice) (Stiff and Haigler, 2016; Pierce et al., 2019; Graham and Haigler, 2021) is obsolete, which led to testing and validation of another low-toxicity, formalin-free fixative (#A5472; Sigma-Aldrich, St. Louis, MO; Fig. S1). The boll wall was removed without damaging the ovules. (Using a razor blade, cut away the top 3 mm of the boll. Make about 1 mm deep longitudinal incisions between the locule walls, and finally cut around the base of the boll.) All of the ovules with attached fiber were lifted out of the locules and fixed (1 h, RT, 1:10 tissue:fixative ratio) prior to optional storage at 4°C. Immediately before imaging, ovules were examined under a stereo microscope (incident light, black background, 31X) to select three vigorous ovules from each boll while avoiding drying. Ovules were rinsed (3 x 5 min) in buffer [0.05 M PIPES, 12 mM EGTA. 5 mM EDTA and 0.1% (w/v) Tween 80, pH 6.8], which had lower osmolarity than a microtubule-stabilizing buffer used previously for aldehyde-fixed fibers (Seagull, 1990; Graham and Haigler, 2021). While steadying an ovule with forceps, one to three small pieces of its chalazal end with attached fibers were dissected away using a small knife (#10055-12; Fine Science Tools, Foster City, CA). Each ovule piece was placed in a single well of a 24-well slide (#63430-04; Electron Microscopy Sciences, Hatfield, PA) containing a single drop of buffer prior to applying and sealing a 24 x 60 mm coverslip with vaseline.

Samples were imaged with brightfield optics and default settings for the 2.83 mega-pixel, color, CCD camera of the Keyence BZ-X810 imaging system (www.keyence.com; housed in the Cellular and Molecular Imaging Facility of NC State). The location of each sample in the 24-well slides was identified visually using a 2X objective and mapped using the navigation function of the integrated Keyence software. Using the 10X objective lens (plan-apochromatic; NA 0.45) and 60% closed condenser aperture setting, a region with many fiber apices was selected for imaging using the multi-point and z-stack capture functions. The precise location was recorded by the software prior to visual setting of the limits of the z-plane range (1.2 µm step size). Typically, three 24-sample slides (representing three accessions) were set up in parallel prior to automatic image capture. The captured z-stacks for each sample were processed into one two-dimensional image using the full-focus function of the software. (Occasional samples contained too much debris for computer vision to be effective, and these were reimaged.)


Resources in this dataset:

  • Resource Title: Deltapine 90 - Manually Annotated Training Set.

    File Name: GH3 DP90 Keyence 1_45 JPEG.zip

    Resource Description: These images were manually annotated in Labelbox.


  • Resource Title: Deltapine 90 - AI-Assisted Annotated Training Set.

    File Name: GH3 DP90 Keyence 46_101 JPEG.zip

    Resource Description: These images were AI-labeled in RoboFlow and then manually reviewed in RoboFlow.


  • Resource Title: Deltapine 90 - Manually Annotated Training-Validation Set.

    File Name: GH3 DP90 Keyence 102_125 JPEG.zip

    Resource Description: These images were manually labeled in LabelBox, and then used for training-validation for the machine learning model.


  • Resource Title: Phytogen 800 - Evaluation Test Images.

    File Name: Gb cv Phytogen 800.zip

    Resource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.


  • Resource Title: Pima 3-79 - Evaluation Test Images.

    File Name: Gb cv Pima 379.zip

    Resource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.


  • Resource Title: Pima S-7 - Evaluation Test Images.

    File Name: Gb cv Pima S7.zip

    Resource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.


  • Resource Title: Coker 312 - Evaluation Test Images.

    File Name: Gh cv Coker 312.zip

    Resource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.


  • Resource Title: Deltapine 90 - Evaluation Test Images.

    File Name: Gh cv Deltapine 90.zip

    Resource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.


  • Resource Title: Half and Half - Evaluation Test Images.

    File Name: Gh cv Half and Half.zip

    Resource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.


  • Resource Title: Fiber Tip Annotations - Manual.

    File Name: manual_annotations.coco_.json

    Resource Description: Annotations in COCO.json format for fibers. Manually annotated in Labelbox.


  • Resource Title: Fiber Tip Annotations - AI-Assisted.

    File Name: ai_assisted_annotations.coco_.json

    Resource Description: Annotations in COCO.json format for fibers. AI annotated with human review in Roboflow.


  • Resource Title: Model Weights (iteration 600).

    File Name: model_weights.zip

    Resource Description: The final model, provided as a zipped Pytorch `.pth` file. It was chosen at training iteration 600. The model weights can be imported for use of the fiber tip type detection neural network in Python.

    Resource Software Recommended: Google Colab,url: https://research.google.com/colaboratory/

Funding

North Carolina State University: Department of Crop and Soil Sciences, N/A

Cotton Incorporated: 10-754

Cotton Incorporated: 18-274

Cotton Incorporated: 21-734

USDA-NIFA: 1015796

USDA: 6066-21310-005-00D

History

Data contact name

Billings, Grant T.

Data contact email

gtbillin@ncsu.edu

Publisher

Ag Data Commons

Intended use

The fiber tip images and annotations may be used to train a machine learning model. The provided PyTorch model can be used to detect two cotton fiber tip types.

Temporal Extent Start Date

2020-08-01

Temporal Extent End Date

2022-04-30

Frequency

  • notPlanned

Theme

  • Not specified

Geographic Coverage

{"type":"FeatureCollection","features":[{"geometry":{"type":"Point","coordinates":[-78.671709001064,35.786662173562]},"type":"Feature","properties":{}}]}

Geographic location - description

Raleigh, North Carolina

ISO Topic Category

  • biota

National Agricultural Library Thesaurus terms

lint cotton; digital images; image analysis; Gossypium; greenhouses; flowers; flowering; ovules; storage temperature; drying; ethylene glycol tetraacetic acid; EDTA (chelating agent); polysorbates; pH; osmolarity; optics; color; cameras; computer software; computer vision; light microscopy; artificial intelligence; neural networks

OMB Bureau Code

  • 005:18 - Agricultural Research Service

OMB Program Code

  • 005:040 - National Research

ARS National Program Number

  • 301

Pending citation

  • No

Public Access Level

  • Public

Preferred dataset citation

Graham, Benjamin P.; Park, Jeremy; Billings, Grant T.; Hulse-Kemp, Amanda M.; Haigler, Candace H.; Lobaton, Edgar (2022). Data from: Efficient imaging and computer vision detection of two cell shapes in young cotton fibers. Ag Data Commons. https://doi.org/10.15482/USDA.ADC/1528324