measure_disease {pliman}

Performs plant disease measurements

Description

• measure_disease() computes the percentage of symptomatic leaf area and (optionally) counts and compute shapes (area, perimeter, radius, etc.) of lesions in a sample or entire leaf using color palettes. See more at Details.

• measure_disease_iter() provides an iterative section for measure_disease(), where the user picks up samples in the image to create the needed color palettes.

Usage

measure_disease(
  img,
  img_healthy = NULL,
  img_symptoms = NULL,
  img_background = NULL,
  pattern = NULL,
  filter = 10,
  parallel = FALSE,
  workers = NULL,
  resize = FALSE,
  fill_hull = TRUE,
  index_lb = NULL,
  index_dh = "GLI",
  has_white_bg = FALSE,
  threshold = NULL,
  invert = FALSE,
  lower_size = NULL,
  upper_size = NULL,
  topn_lower = NULL,
  topn_upper = NULL,
  randomize = TRUE,
  nsample = 3000,
  watershed = FALSE,
  lesion_size = "medium",
  tolerance = NULL,
  extension = NULL,
  show_features = FALSE,
  show_segmentation = FALSE,
  plot = TRUE,
  show_original = TRUE,
  show_background = TRUE,
  show_contour = TRUE,
  contour_col = "white",
  contour_size = 1,
  col_leaf = NULL,
  col_lesions = NULL,
  col_background = NULL,
  marker = FALSE,
  marker_col = NULL,
  marker_size = NULL,
  save_image = FALSE,
  prefix = "proc_",
  name = NULL,
  dir_original = NULL,
  dir_processed = NULL,
  verbose = TRUE
)

measure_disease_iter(
  img,
  has_background = TRUE,
  r = 2,
  viewer = get_pliman_viewer(),
  show = "rgb",
  index = "NGRDI",
  ...
)

Arguments

img

The image to be analyzed.

img_healthy

A color palette of healthy tissues.

img_symptoms

A color palette of lesioned tissues.

img_background

A color palette of the background (if exists). These arguments can be either an Image object stored in the global environment or a character value. If a chacarceter is used (eg., img_healthy = "leaf"), the function will search in the current working directory a valid image that contains "⁠leaf"⁠ in the name. Note that if two images matches this pattern, an error will occour.

pattern

A pattern of file name used to identify images to be processed. For example, if pattern = "im" all images that the name matches the pattern (e.g., img1.-, image1.-, im2.-) will be analyzed. Providing any number as pattern (e.g., pattern = "1") will select images that are named as 1.-, 2.-, and so on.

filter

Performs median filtering in the binary image that segments the leaf from background? By default, a median filter of size = 10 is applied. This is useful to reduce the noise and segment the leaf and background more accurately. See more at image_filter(). Set to FALSE to cancel median filtering.

parallel

Processes the images asynchronously (in parallel) in separate R sessions running in the background on the same machine. It may speed up the processing time, especially when pattern is used is informed. The number of sections is set up to 30% of available cores.

workers

A positive numeric scalar or a function specifying the maximum number of parallel processes that can be active at the same time.

resize

Resize the image before processing? Defaults to FALSE. Use a numeric value of range 0-100 (proportion of the size of the original image).

fill_hull

Fill holes in the image? Defaults to TRUE. This is useful to fill holes in leaves, e.g., those caused by insect attack, ensuring the hole area will be accounted for the leaf, not background.

index_lb

The index used to segment the foreground (e.g., leaf) from the background. If not declared, the entire image area (pixels) will be considered in the computation of the severity.

index_dh

The index used to segment diseased from healthy tissues when img_healthy and img_symptoms are not declared. Defaults to "GLI". See image_index() for more details.

has_white_bg

Logical indicating whether a white background is present. If TRUE, pixels that have R, G, and B values equals to 1 will be considered as NA. This may be useful to compute an image index for objects that have, for example, a white background. In such cases, the background will not be considered for the threshold computation.

threshold

By default (threshold = NULL), a threshold value based on Otsu's method is used to reduce the grayscale image to a binary image. If a numeric value is informed, this value will be used as a threshold. Inform any non-numeric value different than "Otsu" to iteratively choose the threshold based on a raster plot showing pixel intensity of the index. Must be a vector of length 2 to indicate the threshold for index_lb and index_dh, respectively.

invert

Inverts the binary image if desired. This is useful to process images with black background. Defaults to FALSE.

lower_size

Lower limit for size for the image analysis. Leaf images often contain dirt and dust. To prevent dust from affecting the image analysis, the lower limit of analyzed size is set to 0.1, i.e., objects with lesser than 10% of the mean of all objects are removed. One can set a known area or use lower_limit = 0 to select all objects (not advised).

upper_size

Upper limit for size for the image analysis. Defaults to NULL, i.e., no upper limit used.

topn_lower, topn_upper

Select the top n lesions based on its area. topn_lower selects the n lesions with the smallest area whereas topn_upper selects the n lesions with the largest area.

randomize

Randomize the lines before training the model? Defaults to TRUE.

nsample

The number of sample pixels to be used in training step. Defaults to 3000.

watershed

If TRUE (Default) implements the Watershed Algorithm to segment lesions connected by a fairly few pixels that could be considered as two distinct lesions. If FALSE, lesions that are connected by any pixel are considered unique lesions. For more details see EBImage::watershed().

lesion_size

The size of the lesion. Used to automatically tune tolerance and extension parameters. One of the following. "small" (2-5 mm in diameter, e.g, rust pustules), "medium" (0.5-1.0 cm in diameter, e.g, wheat leaf spot), "large" (1-2 cm in diameter, and "elarge" (2-3 cm in diameter, e.g, target spot of soybean).

tolerance

The minimum height of the object in the units of image intensity between its highest point (seed) and the point where it contacts another object (checked for every contact pixel). If the height is smaller than the tolerance, the object will be combined with one of its neighbors, which is the highest. Defaults to NULL, i.e., starting values are set up according to the argument lesion_size.

extension

Radius of the neighborhood in pixels for the detection of neighboring objects. Defaults to 20. Higher value smooths out small objects.

show_features

If TRUE returnS the lesion features such as number, area, perimeter, and radius. Defaults to FALSE.

show_segmentation

Shows the object segmentation colored with random permutations. Defaults to TRUE.

plot

Show image after processing? Defaults to TRUE.

show_original

Show the symptoms in the original image?

show_background

Show the background? Defaults to TRUE. A white background is shown by default when show_original = FALSE.

show_contour

Show a contour line around the lesions? Defaults to TRUE.

contour_col, contour_size

The color and size for the contour line around objects. Defaults to contour_col = "white" and contour_size = 1.

col_leaf

Leaf color after image processing. Defaults to "green"

col_lesions

Symptoms color after image processing. Defaults to "red".

col_background

Background color after image processing. Defaults to "NULL".

marker, marker_col, marker_size

The type, color and size of the object marker. Defaults to NULL, which shows nothing. Use marker = "point" to show a point in each lesion or marker = "*" where "*" is any variable name of the shape data frame returned by the function.

save_image

Save the image after processing? The image is saved in the current working directory named as ⁠proc_*⁠ where * is the image name given in img.

prefix

The prefix to be included in the processed images. Defaults to "proc_".

name

The name of the image to save. Use this to overwrite the name of the image in img.

dir_original, dir_processed

The directory containing the original and processed images. Defaults to NULL. In this case, the function will search for the image img in the current working directory. After processing, when save_image = TRUE, the processed image will be also saved in such a directory. It can be either a full path, e.g., "C:/Desktop/imgs", or a subfolder within the current working directory, e.g., "/imgs".

verbose

If TRUE (default) a summary is shown in the console.

has_background

A logical indicating if the image has a background to be segmented before processing.

r

The radius of neighborhood pixels. Defaults to 2. A square is drawn indicating the selected pixels.

viewer

The viewer option. If not provided, the value is retrieved using get_pliman_viewer(). This option controls the type of viewer to use for interactive plotting. The available options are "base" and "mapview". If set to "base", the base R graphics system is used for interactive plotting. If set to "mapview", the mapview package is used. To set this argument globally for all functions in the package, you can use the set_pliman_viewer() function. For example, you can run set_pliman_viewer("mapview") to set the viewer option to "mapview" for all functions.

show

The show option for the mapview viewer, either "rgb" or "index".

index

The index to be shown when show = "rgb".

...

Further parameters passed on to measure_disease().

Details

In measure_disease(), 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. By using pattern it is possible to process several images with common pattern names that are stored in the current working directory or in the subdirectory informed in dir_original.

If img_healthy and img_symptoms are not declared, RGB-based phenotyping of foliar disease severity is performed using the index informed in index_lb to first segment leaf from background and index_dh to segment diseased from healthy tissues.

measure_disease_iter() only run in an interactive section. In this function, users will be able to pick up samples of images to iteratively create the needed color palettes. This process calls pick_palette() internally. If has_background is TRUE (default) the color palette for the background is first created. The sample of colors is performed in each left-button mouse click and continues until the user press Esc. Then, a new sampling process is performed to sample the color of healthy tissues and then diseased tissues. The generated palettes are then passed on to measure_disease(). All the arguments of such function can be passed using the ... (three dots).

When show_features = TRUE, the function computes a total of 36 lesion features (23 shape features and 13 texture features). The Haralick texture features for each object based on a gray-level co-occurrence matrix (Haralick et al. 1979). See more details in analyze_objects().

Value

• measure_disease() returns a list with the following objects:

  • severity A data frame with the percentage of healthy and symptomatic areas.

  • shape,statistics If show_features = TRUE is used, returns the shape (area, perimeter, etc.) for each lesion and a summary statistic of the results.

• measure_disease_iter() returns a list with the following objects:

  • results A list with the objects returned by measure_disease().

  • leaf The color palettes for the healthy leaf.

  • disease The color palettes for the diseased leaf.

  • background The color palettes for the background.

Author(s)

Tiago Olivoto [e-mail]

Examples

library(pliman)
img <- image_pliman("sev_leaf_nb.jpg")
healthy <- image_pliman("sev_healthy.jpg")
lesions <- image_pliman("sev_sympt.jpg")
image_combine(img, healthy, lesions, ncol = 3)

sev <-
 measure_disease(img = img,
                 img_healthy = healthy,
                 img_symptoms = lesions,
                 lesion_size = "large",
                 plot = TRUE)

# an interactive section
measure_disease_iter(img)