pliman

The pliman (plant image analysis) package is designed to analyze plant images, particularly for leaf and seed analysis. It offers a range of functionalities to assist with various tasks such as measuring disease severity, counting lesions, obtaining lesion shapes, counting objects in an image, extracting object characteristics, performing Fourier Analysis, obtaining RGB values, extracting object coordinates and outlines, isolating objects, and plotting object measurements.

pliman also provides useful functions for image transformation, binarization, segmentation, and resolution. Please visit the Examples page on the pliman website for 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 using the pak package:

pak::pkg_install("TiagoOlivoto/pliman")

Note: If you are a Windows user, you should also first download and install the latest version of Rtools.

Analyze objects

The function analyze_objects() can be used to analyze objects such as leaves, grains, pods, and pollen in an image. By default, all measures are returned in pixel units. Users can adjust the object measures with get_measures() provided that the image resolution (Dots Per Inch) is known. Another option is to use a reference object in the image. In this last case, the argument reference must be set to TRUE. There are two options to identify the reference object:

1. By its color, using the arguments back_fore_index and fore_ref_index
2. By its size, using the arguments reference_larger or reference_smaller

In both cases, the reference_area must be declared. Let’s see how to analyze an image with flax grains containing a reference object (rectangle with 2x3 cm). Here, we’ll identify the reference object by its size; so, the final results in this case will be in metric units (cm).

library(pliman)
img <- image_pliman("flax_grains.jpg")
flax <- 
  analyze_objects(img,
                  index = "GRAY",
                  reference = TRUE,
                  reference_larger = TRUE,
                  reference_area = 6,
                  marker = "point",
                  marker_size = 0.5,
                  marker_col = "red", # default is white
                  show_contour = FALSE) # default is TRUE

# summary statistics
flax$statistics
#        stat        value
# 1         n 2.680000e+02
# 2  min_area 3.606989e-02
# 3 mean_area 6.250403e-02
# 4  max_area 1.262446e-01
# 5   sd_area 8.047152e-03
# 6  sum_area 1.675108e+01
# 7  coverage 5.388462e-02

# plot the density of the grain's length (in cm)
plot(flax, measure = "length")

Analyzing orthomosaics

Counting and measuring plants within plots

Here, I used mosaic_analyze() to count, measure, and extract image indexes at block, plot, and individual levels using an orthomosaic from a lettuce trial available in this paper. By using segment_individuals = TRUE, a deeper analysis is performed at individual levels, which enables counting and measuring the plants within plots. To reproduce, download the lettuce mosaic, and follow the tutorial below.

library(pliman)
set_wd_here() # set the directory to the file location
mo <- mosaic_input("lettuce.tif")
indexes <- c("NGRDI", "GLI", "SCI", "BI", "VARI", "EXG", "MGVRI")
# draw four blocks of 12 plots
an <- mosaic_analyze(mo,
           r = 1, g = 2, b = 3,
           nrow = 12,
           segment_individuals = TRUE,
           segment_index = "NGRDI",
           plot_index = indexes)

Canopy coverage and multispectral indexes

In this example, a multispectral orthomosaic originally available here is used to show how mosaic_analyze() can be used to compute the plot coverage and statistics such as min, mean, and max values of three multispectral indexes (NDVI, EVI, and NDRE) using a design that includes 6 rows and 15 plots per row. To reproduce, download the orthomosaic, save it within the current workind directory, and follow the tutorial below.

library(pliman)
set_wd_here() # set the directory to the file location
mosaic <- mosaic_input("ndsu.tif")

res <- 
  mosaic_analyze(mosaic,
                 nrow = 3,  # use 6 if you want to analyze in a single block
                 ncol = 15,
                 buffer_row = -0.15,
                 buffer_col = -0.05,
                 segment_plot = TRUE,
                 segment_index = "NDVI", 
                 plot_index = c("NDVI", "EVI", "NDRE"), 
                 summarize_fun = c("min", "mean", "max"),
                 attribute = "coverage")
res$map_plot

Counting and measuring distance betwen plants

In this example, an RGB orthomosaic from a rice field originally available here is used to show how mosaic_analyze() can be used to count plants and measure the distance between plants within each plot. The first step is to build the plots. By default a grid (grid = TRUE) is build according to the nrow and ncol arguments. In this step, use the “Drawn polygon” button to drawn a polygon that defines the area to be analyzed. After drawing the polygon, click “Done”. When the argument check_shapefile = TRUE (default) is used, users can check if the plots were correctly drawn. In this step, it is also possible to a live edition of the plots by clicking on “edit layers” button. After the changes are made, don’t forget to click “Save”. To remove any plot, just click on “Delete layers” button, followed by “Save”. After all the editions are made, click “Done”. The function will follow the mosaic analysis using the edited shapefile. After the mosaic has been analyzed, a plot is produced by default. In this plot, individuals are highlighted with a color scale showing the area of each individual. The results on both plot- and individual level are stored in data frames that can be easily exported for further analysis

To reproduce, download the rice_ex.tif mosaic, save it within the current working directory, and follow the tutorial below.

library(pliman)
set_wd_here() # set the directory to the file location
mosaic <- mosaic_input("rice_ex.tif")

res <- 
  mosaic_analyze(mosaic,
                 r = 1, g = 2, b = 3,
                 segment_individuals = TRUE,
                 segment_index = "(G-B)/(G+B-R)",
                 filter = 4,
                 nrow = 8,
                 map_individuals = TRUE)

Using an external shapefile

When a shapefile is provided there is no need to build the plots, since the function will analyze the mosaic assuming the geometries provided by the shapefile. To reproduce, download the mosaic and shapefile needed, save them within the current working directory and follow the scripts below.

library(pliman)
set_wd_here() # set the directory to the file location
# Import the mosaic
mosaic <- mosaic_input("rice_ex.tif")
# Import the shapefile
shapefile <- shapefile_input("rice_ex_shp.rds")

# analyze the mosaic using the shapefile
res <- 
  mosaic_analyze(mosaic,
                 r = 1, g = 2, b = 3,
                 shapefile = shapefile,
                 segment_individuals = TRUE,
                 segment_index = "(G-B)/(G+B-R)",
                 filter = 4,
                 map_individuals = TRUE)
# Distances between individuals within each plot
str(res$result_individ_map)

# plot-level results
str(res$result_plot_summ)

# individua-level results
str(res$result_indiv)

Disease severity

Using image indexes

To compute the percentage of symptomatic leaf area you can use the measure_disease() function you can use an image index to segment the entire leaf from the background and then separate the diseased tissue from the healthy tissue. Alternatively, you can provide color palette samples to the measure_disease() function. In this approach, the function fits a general linear model (binomial family) to the RGB values of the image. It then uses the color palette samples to segment the lesions from the healthy leaf.

In the following example, we compute the symptomatic area of a soybean leaf. The proportion of healthy and symptomatic areas is given as a proportion of the total leaf area after segmenting the leaf from the background (blue).

img <- image_pliman("sev_leaf.jpg")
# Computes the symptomatic area
sev <- 
measure_disease(img = img,
                index_lb = "B", # to remove the background
                index_dh = "NGRDI", # to isolate the diseased area
                threshold = c("Otsu", 0), # You can also use the Otsu algorithm in both indexes (default)
                plot = TRUE)

sev$severity
#    healthy symptomatic
# 1 92.62721    7.372791

Interactive disease measurements

An alternative approach to measuring disease percentage is available through the measure_disease_iter() function. This function offers an interactive interface that empowers users to manually select sample colors directly from the image. By doing so, it provides a highly customizable analysis method.

One advantage of using measure_disease_iter() is the ability to utilize the “mapview” viewer, which enhances the analysis process by offering zoom-in options. This feature allows users to closely examine specific areas of the image, enabling detailed inspection and accurate disease measurement.

img <- image_pliman("sev_leaf.jpg", plot = TRUE)

# works only in an interactive section
measure_disease_iter(img, viewer = "mapview")

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 in Ecology and Evolution_, *13*(4),
  789-798. doi:10.1111/2041-210X.13803
  <https://doi.org/10.1111/2041-210X.13803>.

Uma entrada BibTeX para usuários(as) de LaTeX é

  @Article{,
    title = {Lights, camera, pliman! An R package for plant image analysis},
    author = {Tiago Olivoto},
    year = {2022},
    journal = {Methods in Ecology and Evolution},
    volume = {13},
    number = {4},
    pages = {789-798},
    doi = {10.1111/2041-210X.13803},
  }

Getting help

If you come across any clear bugs while using the package, please consider filing a minimal reproducible example on github. This will help the developers address the issue promptly.

Suggestions and criticisms aimed at improving the quality and usability of the package are highly encouraged. Your feedback is valuable in making {pliman} even better!

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.