pliman (plant image analysis) is designed to analyze plant images, especially related to leaf analysis. You provide color palettes, tell pliman what each one represents, and it takes care of the details. Image indexes can also be used to segment images. The package will help you to:
- Measure leaf area;
- Measure disease severity;
- Count the number of lesions;
- Obtain the shape of lesions;
- Produce Standard Area Diagrams;
- Count objects in an image;
- Get object features (area, perimeter, radius, circularity, eccentricity, solidity);
- Get the RGB values for each object in an image;
- Get the object coordinates;
- Get the object contours;
- Get convex hulls;
- Isolate objects;
- Plot object measures;
- Perform Elliptical Fourier Analysis;
pliman also provides useful functions for image transformation, binarization, segmentation, and resolution. Please, visit the Examples page in pliman website for a detailed documentation of each function.
Installation
Install the latest stable version of pliman from CRAN with:
install.packages("pliman")The development version of pliman can be installed from GitHub with:
devtools::install_github("TiagoOlivoto/pliman")
# To build the HTML vignette use
devtools::install_github("TiagoOlivoto/pliman", build_vignettes = TRUE)Note: If you are a Windows user, you should also first download and install the latest version of Rtools.
Basic usage
The two main functions of pliman are analyze objects() and measure disease(), which can be used to compute object features and measure disease severity, respectively.
Analyze objects
The function analyze_objects() can be used to analyze objects such as leaves, grains, pods, and pollen in an image. The following example counts and computes several features of soybean grains of an image with 30 grains.
library(pliman)
# |==========================================================|
# | Tools for Plant Image Analysis (pliman 1.2.0) |
# | Author: Tiago Olivoto |
# | Type 'citation('pliman')' to know how to cite pliman |
# | Type 'vignette('pliman_start')' for a short tutorial |
# | Visit 'http://bit.ly/pkg_pliman' for a complete tutorial |
# |==========================================================|
img <-image_pliman("soybean_touch.jpg", plot = TRUE)
soy <- analyze_objects(img, marker = "id")
str(soy$results)
# 'data.frame': 30 obs. of 47 variables:
# $ id : num 1 2 3 4 5 6 7 8 9 10 ...
# $ x : num 246 538 238 345 278 ...
# $ y : num 510 402 340 106 261 ...
# $ area : num 2275 2299 2311 2443 2161 ...
# $ area_ch : num 2302 2270 2289 2413 2123 ...
# $ perimeter : num 184 178 181 186 172 ...
# $ radius_mean : num 26.5 26.6 26.7 27.5 25.8 ...
# $ radius_min : num 22.9 25 24 24.3 24.2 ...
# $ radius_max : num 29.4 28.7 29.4 30.5 28 ...
# $ radius_sd : num 1.369 0.945 1.24 1.757 0.803 ...
# $ diam_mean : num 52.9 53.2 53.4 55 51.5 ...
# $ diam_min : num 45.9 49.9 48 48.7 48.5 ...
# $ diam_max : num 58.9 57.4 58.9 61.1 56.1 ...
# $ major_axis : num 56.1 56.5 57.5 60.9 54.1 ...
# $ minor_axis : num 51.9 51.9 51.3 51.1 50.9 ...
# $ caliper : num 57.3 56.9 57.7 61 54.4 ...
# $ length : num 56.6 56.5 57.2 61 54 ...
# $ width : num 51.6 52.4 52 51 50.6 ...
# $ radius_ratio : num 1.28 1.15 1.23 1.25 1.16 ...
# $ theta : num -0.889 -0.841 -0.565 -0.993 -0.218 ...
# $ eccentricity : num 0.893 0.853 0.816 0.754 0.896 ...
# $ form_factor : num 0.849 0.91 0.886 0.891 0.921 ...
# $ narrow_factor : num 1.01 1.01 1.01 1 1.01 ...
# $ asp_ratio : num 1.1 1.08 1.1 1.2 1.07 ...
# $ rectangularity : num 1.28 1.29 1.29 1.27 1.26 ...
# $ pd_ratio : num 3.2 3.13 3.14 3.04 3.16 ...
# $ plw_ratio : num 1.7 1.64 1.66 1.66 1.64 ...
# $ solidity : num 0.988 1.013 1.01 1.012 1.018 ...
# $ convexity : num 0.886 0.881 0.911 0.915 0.898 ...
# $ elongation : num 0.0884 0.0728 0.0911 0.1638 0.0642 ...
# $ circularity : num 14.8 13.8 14.2 14.1 13.6 ...
# $ circularity_haralick: num 19.3 28.1 21.5 15.7 32.1 ...
# $ circularity_norm : num 1.22 1.14 1.17 1.16 1.13 ...
# $ coverage : num 0.00426 0.0043 0.00432 0.00457 0.00404 ...
# $ asm : num 0.0679 0.0886 0.0848 0.0869 0.0865 ...
# $ con : num 0.527 0.603 0.702 0.73 0.834 ...
# $ cor : num 0.961 0.959 0.95 0.945 0.944 ...
# $ var : num 7.68 8.37 7.98 7.58 8.46 ...
# $ idm : num 0.81 0.807 0.794 0.801 0.795 ...
# $ sav : num 40.5 40.7 40.7 40.6 40.9 ...
# $ sva : num 1566 1591 1590 1583 1606 ...
# $ sen : num 1.24 1.18 1.17 1.17 1.2 ...
# $ ent : num 1.4 1.36 1.36 1.36 1.4 ...
# $ dva : num 0.527 0.603 0.702 0.73 0.834 ...
# $ den : num 0.347 0.364 0.386 0.385 0.392 ...
# $ f12 : num 0.569 0.56 0.535 0.544 0.542 ...
# $ f13 : num 0.82 0.807 0.793 0.798 0.804 ...Disease severity
img <-image_pliman("sev_leaf.jpg")
healthy <-image_pliman("sev_healthy.jpg")
symptoms <-image_pliman("sev_sympt.jpg")
background <-image_pliman("sev_back.jpg")
image_combine(img, healthy, symptoms, background, ncol = 4)
sev <-
measure_disease(img = img,
img_healthy = healthy,
img_symptoms = symptoms,
img_background = background)
sev$severity
# healthy symptomatic
# 1 89.20609 10.79391pliman takes the advantage of several powerful functions from EBImage package. Thanks to Andrzej Oleś and collaborators for the impressive job done with EBImage!
Citation
citation("pliman")
Please, support this project by citing it in your publications!
Olivoto, T.(2022). Lights, camera, pliman! An R package for plant
image analysis. Methods Ecol Evol. 13:789-798
doi:10.1111/2041-210X.13803
A BibTeX entry for LaTeX users is
@Article{Olivoto2022,
author = {Tiago Olivoto},
title = {Lights, camera, pliman! An R package for plant image analysis},
journal = {Methods in Ecology and Evolution},
volume = {13},
number = {4},
pages = {789-798},
year = {2022},
doi = {10.1111/2041-210X.13803},
}Code of Conduct
Please note that the pliman project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.