Plant IMage Analysis

Tiago Olivoto

2021-11-09

Getting started

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. The package will help you to:

Leaf area

The function analyze_objects() can be used to measure the leaf area in an image. The pixel area can be adjusted to metric units (cm) in two ways: (i) Using an object of known area to correct the measures or (ii) knowing the image resolution in dpi (dots per inch).

To show the first approach, first we count the number of objects and plot an object id using the argument marker = "text" of the function analyze_objects(). This allows for the further adjustment of the leaf area using the known object, that in this case is the leaf square, with 4 cm\(^2\).

library(pliman)
# |=======================================================|
# | Tools for Plant Image Analysis (pliman 1.0.0)         |
# | Author: Tiago Olivoto                                 |
# | Type 'vignette('pliman_start')' for a short tutorial  |
# | Visit 'https://bit.ly/pliman' for a complete tutorial |
# |=======================================================|
leaves <- image_pliman("la_leaves.jpg")
plot(leaves)
count <- analyze_objects(leaves, marker = "id")

The function get_measures() is used to adjust the leaf area using object 6.

area <- 
  get_measures(count,
               id = 6,
               area ~ 4)
# -----------------------------------------
# measures corrected with:
# object id: 6
# area     : 4
# -----------------------------------------
# Total    : 149.331 
# Average  : 24.888 
# -----------------------------------------
area
#   id       x       y   area area_ch perimeter radius_mean radius_min radius_max
# 1  1 537.380 498.981 41.716  41.895    22.161       3.695      2.768      5.279
# 2  2 438.655 165.253 35.724  35.858    19.502       3.386      2.891      4.590
# 3  3 110.862 477.018 31.584  32.438    20.174       3.284      2.389      4.897
# 4  4 178.467 174.227 27.697  28.096    18.157       3.039      2.311      4.405
# 5  5 315.232 434.602  8.608   8.614     9.782       1.664      1.319      2.271
# 6  6 313.445 655.336  4.000   4.008     7.672       1.125      0.929      1.371
#   radius_sd radius_ratio diam_mean diam_min diam_max major_axis minor_axis
# 1    21.561        1.907     7.391    5.536   10.558      9.196      0.281
# 2    13.655        1.588     6.772    5.782    9.181      7.891      0.241
# 3    21.404        2.050     6.569    4.779    9.794      7.989      0.244
# 4    17.091        1.906     6.077    4.623    8.810      7.242      0.221
# 5     8.608        1.721     3.328    2.639    4.542      4.095      0.125
# 6     3.862        1.475     2.250    1.859    2.742      2.394      0.073
#   eccentricity  theta solidity circularity
# 1        0.767  1.538    0.996       1.067
# 2        0.675 -1.533    0.996       1.180
# 3        0.758  1.545    0.974       0.975
# 4        0.723  1.539    0.986       1.056
# 5        0.752 -1.467    0.999       1.131
# 6        0.380 -1.550    0.998       0.854
# plot the area to the segmented image
image_segment(leaves, index = "NB", verbose = FALSE)
plot_measures(area, measure = "area")

When the image resolution is known, we can the image dpi (dots per inch) to correct the pixel units given by analyze_objects() to metric units. The function dpi() can be used to compute the dpi of an image, provided that the size of any object is known. See a brief tutorial here.

get_measures(count, dpi = 84)
#   id       x       y   area area_ch perimeter radius_mean radius_min radius_max
# 1  1 537.380 498.981 40.823  40.997    21.923       3.656      2.738      5.222
# 2  2 438.655 165.253 34.959  35.090    19.292       3.350      2.860      4.541
# 3  3 110.862 477.018 30.908  31.743    19.957       3.249      2.364      4.844
# 4  4 178.467 174.227 27.104  27.494    17.961       3.006      2.286      4.357
# 5  5 315.232 434.602  8.424   8.429     9.676       1.646      1.305      2.247
# 6  6 313.445 655.336  3.914   3.923     7.590       1.113      0.919      1.356
#   radius_sd radius_ratio diam_mean diam_min diam_max major_axis minor_axis
# 1    21.561        1.907     7.311    5.476   10.445      9.097      0.275
# 2    13.655        1.588     6.700    5.720    9.082      7.806      0.236
# 3    21.404        2.050     6.498    4.727    9.689      7.903      0.239
# 4    17.091        1.906     6.012    4.573    8.715      7.164      0.217
# 5     8.608        1.721     3.292    2.610    4.493      4.051      0.122
# 6     3.862        1.475     2.226    1.839    2.712      2.368      0.072
#   eccentricity  theta solidity circularity
# 1        0.767  1.538    0.996       1.067
# 2        0.675 -1.533    0.996       1.180
# 3        0.758  1.545    0.974       0.975
# 4        0.723  1.539    0.986       1.056
# 5        0.752 -1.467    0.999       1.131
# 6        0.380 -1.550    0.998       0.854

Counting crop grains

Here, we will count the grains in the image soybean_touch.jpg. This image has a cyan background and contains 30 soybean grains that touch with each other.

soy <- image_pliman("soybean_touch.jpg")
plot(soy)

# Count the objects in the image
grains <- analyze_objects(soy)

# Draws the object id (by default)
plot_measures(grains)

Disease severity

The function measure_disease() is used to compute the percentage of symptomatic leaf area in a sample or entire leaf based on provided color palettes samples. A general linear model (binomial family) fitted to the RGB values is used to segment the lesions from the healthy leaf. If a pallet of background is provided, the function takes care of the details to isolate it before computing the number and area of lesions.

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

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)


# Computes the symptomatic area
measure_disease(img = img,
                img_healthy = healthy,
                img_symptoms = symptoms,
                img_background = background,
                show_image = TRUE)

# $severity
#    healthy symptomatic
# 1 88.83739    11.16261
# 
# $shape
# NULL
# 
# $statistics
# NULL
# 
# attr(,"class")
# [1] "plm_disease"

RGB values for objects

The function objects_rgb() can be used to get the Red Green and Blue (RGB) for objects in an image. Here, the RGB for

img <- image_pliman("soy_green.jpg")
# Segment the foreground (grains) using the normalized blue index
# Shows the average value of the blue index in each object

rgb <- 
  analyze_objects(img,
                  object_index = "B",
                  marker = "index")
# Warning: Accepted 'marker' are: {id, x, y, area, area_ch, perimeter,
# radius_mean, radius_min, radius_max, radius_sd, radius_ratio, diam_mean,
# diam_min, diam_max, major_axis, minor_axis, eccentricity, theta, solidity,
# circularity}. Drawing the object id.



# plot the distribution of RGB values of each object
plot(rgb)

Getting help