In TiagoOlivoto/pliman: Tools for Plant Image Analysis

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  rmarkdown.html_vignette.check_title = FALSE
)

Getting started

Single images

library(pliman)
# set the path directory
path_soy <- "https://raw.githubusercontent.com/TiagoOlivoto/images/master/pliman"
# import images
img <- image_import("leaf.jpg", path = path_soy)
healthy <- image_import("healthy.jpg", path = path_soy)
symptoms <- image_import("sympt.jpg", path = path_soy)
background <- image_import("back.jpg", path = path_soy)
image_combine(img, healthy, symptoms, background, ncol = 4)

Image palettes

Sample palettes can be created by manually sampling small areas of representative images and producing a composite image that represents each of the desired classes (background, healthy, and symptomatic tissues). Another approach is to use the image_palette() function to generate sample color palettes.

pals <- image_palette(img, npal = 8)
image_combine(pals$palette_list)
# to extract the color palettes, use the object
plot(pals$palette_list[[1]])

# default settings
res <-
  measure_disease(img = img,
                  img_healthy = healthy,
                  img_symptoms = symptoms,
                  img_background = background)
res$severity

Alternatively, users can create a mask instead of displaying the original image.

# create a personalized mask
res2 <- 
  measure_disease(img = img,
                  img_healthy = healthy,
                  img_symptoms = symptoms,
                  img_background = background,
                  show_original = FALSE, # create a mask
                  show_contour = FALSE, # hide the contour line
                  col_background = "white", # default
                  col_lesions = "red", # default
                  col_leaf = "green") # default


res2$severity

Variations in image palettes

The results may vary depending on how the palettes are chosen and are subjective due to the researcher's experience. In the following example, I present a second variation in the color palettes, where only the necrotic area is assumed to be the diseased tissue. Therefore, the symptomatic area will be smaller than in the previous example.

# import images
healthy2 <- image_import("healthy2.jpg", path = path_soy)
symptoms2 <- image_import("sympt2.jpg", path = path_soy)
background2 <- image_import("back2.jpg", path = path_soy)
image_combine(healthy2, symptoms2, background2, ncol = 3)

res3 <-
  measure_disease(img = img,
                  img_healthy = healthy2,
                  img_symptoms = symptoms2,
                  img_background = background2)
res3$severity

Lesion features

res4 <-
  measure_disease(img = img,
                  img_healthy = healthy,
                  img_symptoms = symptoms,
                  img_background = background,
                  show_features = TRUE,
                  marker = "area")
res4$shape
res4$statistics

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)
measure_disease_iter(img, viewer = "mapview")

A little bit more!

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At this link you will find more examples on how to use {pliman} to analyze plant images. Source code and images can be downloaded here. You can also find a talk (Portuguese language) about {pliman} here. Lights, camera, {pliman}!

TiagoOlivoto/pliman documentation built on Sept. 14, 2024, 2:24 a.m.