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Performs plant disease measurements by leaf

Source: R/measure_disease_byl.R
measure_disease_byl.Rd

Computes the percentage of symptomatic leaf area using color palettes or RGB indexes by each leaf of an image. This allows, for example, processing replicates of the same treatment and obtaining the results for each replication with a single image. To do that, leaf samples are first splitten with object_split() and then, measure_disease() is applied to the list of leaves.

Usage

measure_disease_byl(
  img,
  index = "B",
  index_lb = "B",
  index_dh = "NGRDI",
  lower_size = NULL,
  watershed = TRUE,
  invert = FALSE,
  fill_hull = FALSE,
  filter = 3,
  threshold = "Otsu",
  extension = NULL,
  tolerance = NULL,
  object_size = "large",
  img_healthy = NULL,
  img_symptoms = NULL,
  plot = TRUE,
  save_image = FALSE,
  dir_original = NULL,
  dir_processed = NULL,
  pattern = NULL,
  parallel = FALSE,
  workers = NULL,
  show_features = FALSE,
  verbose = TRUE,
  ...
)

Arguments

img

The image to be analyzed.

index

A character value specifying the target mode for conversion to binary to segment the leaves from background. Defaults to "B" (blue). See image_index() for more details. Personalized indexes can be informed as, e.g., index = "R*G/B.

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.

lower_size

To prevent dust from affecting object segmentation, 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).

watershed

If TRUE (default) performs watershed-based object detection. This will detect objects even when they are touching one other. If FALSE, all pixels for each connected set of foreground pixels are set to a unique object. This is faster but is not able to segment touching objects.

invert

Inverts the binary image if desired. This is useful to process images with a black background. Defaults to FALSE. If reference = TRUE is use, invert can be declared as a logical vector of length 2 (eg., invert = c(FALSE, TRUE). In this case, the segmentation of objects and reference from the foreground using back_fore_index is performed using the default (not inverted), and the segmentation of objects from the reference is performed by inverting the selection (selecting pixels higher than the threshold).

fill_hull

Fill holes in the binary image? Defaults to FALSE. This is useful to fill holes in objects that have portions with a color similar to the background. IMPORTANT: Objects touching each other can be combined into one single object, which may underestimate the number of objects in an image.

filter

Performs median filtering in the binary image? See more at image_filter(). Defaults to FALSE. Use a positive integer to define the size of the median filtering. Larger values are effective at removing noise, but adversely affect edges.

threshold

The theshold method to be used.

  • By default (threshold = "Otsu"), 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.

  • If threshold = "adaptive", adaptive thresholding (Shafait et al. 2008) is used, and will depend on the k and windowsize arguments.

  • If any non-numeric value different than "Otsu" and "adaptive" is used, an iterative section will allow you to choose the threshold based on a raster plot showing pixel intensity of the index.

extension

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

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.

object_size

The size of the object. Used to automatically set up tolerance and extension parameters. One of the following. "small" (e.g, wheat grains), "medium" (e.g, soybean grains), "large"(e.g, peanut grains), and "elarge" (e.g, soybean pods)`.

img_healthy

A color palette of healthy tissues.

img_symptoms

A color palette of lesioned tissues.

plot

Show image after processing?

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.

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".

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.

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.

show_features

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

verbose

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

...

Additional arguments passed on to measure_disease().

Value

  • A list with the following objects:

    • severity A data frame with the percentage of healthy and symptomatic areas for each leaf in the image(s).

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

Examples

library(pliman)
img <- image_pliman("mult_leaves.jpg", plot = TRUE)

sev <-
 measure_disease_byl(img = img,
                     index_lb = "B",
                     index_dh = "NGRDI",
                     workers = 2)



sev$severity
#>   img leaf  healthy symptomatic
#> 1 img    1 84.36079    15.63921
#> 2 img    2 79.76666    20.23334
#> 3 img    3 68.39437    31.60563