kiwisorter

Piani et. al 2023, PMOV Tatura (AUS)

https://github.com/ecopom/kiwisorter

Science Score: 49.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
    Found 1 DOI reference(s) in README
  • Academic publication links
    Links to: zenodo.org
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (1.9%) to scientific vocabulary
Last synced: 6 months ago · JSON representation

Repository

Piani et. al 2023, PMOV Tatura (AUS)

Basic Info
  • Host: GitHub
  • Owner: ECOPOM
  • License: gpl-3.0
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 634 MB
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 2
Created about 2 years ago · Last pushed about 2 years ago
Metadata Files
Readme License Citation

README.md

Piani et al 2023, in press.

Acta repository for Piani et. al. 2023, "Red-flesh kiwifruit inner quality scoring with a computer vision system"

authors:

Piani M., Bortolotti G., Mengoli D., Omodei N., Raule N., Spinelli F., Manfrini L. \ DOI

Paper presented at ISHS PMOV 2023, Tatura, State of Victoria Alt text

Owner

  • Name: ECOPOM
  • Login: ECOPOM
  • Kind: organization

GitHub Events

Total
  • Watch event: 1
Last Year
  • Watch event: 1

Dependencies

requirements.txt pypi
  • Brotli ==1.0.9
  • Cython ==3.0.0
  • Deprecated ==1.2.14
  • Flask ==2.3.2
  • Jinja2 ==3.1.2
  • Markdown ==3.4.4
  • MarkupSafe ==2.1.2
  • Pillow ==9.3.0
  • PyJWT ==2.4.0
  • PyWavelets ==1.4.1
  • PyYAML ==6.0
  • Pygments ==2.15.1
  • QtPy ==2.3.1
  • Send2Trash ==1.8.2
  • Werkzeug ==2.3.7
  • absl-py ==1.4.0
  • aggdraw ==1.3.16
  • aiofiles ==23.1.0
  • anyio ==3.7.1
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  • arrow ==1.2.3
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  • cffi ==1.15.1
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  • orderedmultidict ==1.0.1
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  • requests ==2.28.1
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  • scikit-image ==0.21.0
  • scikit-learn ==1.3.0
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