Package: grayleafspotr 0.99.2

grayleafspotr: Quantitative Analysis of Gray Leaf Spot Colonies from Plate Images

Quantitative phenotyping of gray leaf spot (Cercospora zeae-maydis) fungal colonies grown on petri dishes. The package segments colonies from time-lapse plate photographs using a bundled SmallUNet deep-learning model, extracts morphometric and texture features (area, eccentricity, crack coverage, radial profile), and provides tidy result objects together with template ggplot2 visualisations. Python dependencies are managed automatically through basilisk; no manual environment setup is required.

Authors:Rohan R [aut, cre, cph]

grayleafspotr_0.99.2.tar.gz
grayleafspotr_0.99.2.zip(r-4.7)grayleafspotr_0.99.2.zip(r-4.6)grayleafspotr_0.99.2.zip(r-4.5)
grayleafspotr_0.99.2.tgz(r-4.6-any)grayleafspotr_0.99.2.tgz(r-4.5-any)
grayleafspotr_0.99.2.tar.gz(r-4.7-any)grayleafspotr_0.99.2.tar.gz(r-4.6-any)
grayleafspotr_0.99.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
grayleafspotr/json (API)

# Install 'grayleafspotr' in R:
install.packages('grayleafspotr', repos = c('https://rotsl.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/rotsl/grayleafspotr/issues

Pkgdown/docs site:https://rotsl.github.io

On CRAN:

Conda:

softwarepreprocessingvisualizationclassificationfeatureextractionbiological-data-analysisgrayleafspothuggingfaceimage-processingmlpythonsegmentation

3.48 score 1 stars 7 scripts 18 exports 48 dependencies

Last updated from:6a87fafd1c. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK223
source / vignettesOK218
linux-release-x86_64OK167
macos-release-arm64OK104
macos-oldrel-arm64OK107
windows-develOK123
windows-releaseOK99
windows-oldrelOK95
wasm-releaseOK138

Exports:as_grayleafspot_growth_dataexample_grayleafspot_resultsgrayleafspot_analyzegrayleafspot_download_modelgrayleafspot_python_availablegrayleafspot_python_executablegrayleafspot_runlaunch_grayleafspotrplot_colony_expansionplot_feature_heatmapplot_growth_roughnessplot_radial_growth_areaplot_radial_profileplot_shape_vs_stressplot_stress_remodelingplot_texture_organizationread_grayleafspot_resultswrite_grayleafspot_results

Dependencies:basiliskbitbit64clicliprcpp11crayondir.expirydplyrfarverfilelockgenericsggplot2gluegtableherehmsisobandjsonlitelabelinglatticelifecyclemagrittrMatrixpillarpkgconfigpngprettyunitsprogressR6rappdirsRColorBrewerRcppRcppTOMLreadrreticulaterlangrprojrootS7scalestibbletidyselecttzdbutf8vctrsviridisLitevroomwithr

Getting Started with grayleafspotr
What is grayleafspotr? | Quick start with bundled data | View the feature table | Template plots | Convert to a plain data frame | Analyze your own images | Reload saved results | Next steps
Rendered fromgetting-started.Rmdusingknitr::rmarkdown

Last update: 2026-05-27
Started: 2026-04-28

grayleafspotr Workflow
Abstract | 1. Package overview | What the package does | Input requirements | Workflow at a glance | 2. Installation | 3. Bundled example data | 3.1 Load the example run | 3.2 Inspect the feature table | 3.3 Locate bundled source images | 4. Template visualisations | Colony expansion over time | Growth rate and edge roughness | Crack coverage and count | Texture organisation | Shape versus stress | Feature correlation heatmap | Radial intensity profile | 5. Work with tidy data | Custom plot | 6. Analyze your own images | 6.1 Prepare your image folder | 6.2 Run the analysis — simple entry point | 6.3 Full-featured alternative: grayleafspot_analyze() | 6.4 Reload saved results | 7. Developer note: Python override | Session information
Rendered fromgrayleafspotr-workflow.Rmdusingknitr::rmarkdown

Last update: 2026-05-27
Started: 2026-04-28