pliman

pliman R package

https://github.com/nepem-ufsc/pliman

Repository

pliman R package

README.Rmd

---
always_allow_html: yes
output: github_document
---



```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

```

# pliman 



[![CRAN status](https://www.r-pkg.org/badges/version-ago/pliman)](https://CRAN.R-project.org/package=pliman) [![Lifecycle: stable](https://lifecycle.r-lib.org/articles/figures/lifecycle-stable.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable) ![Total Downloads](https://cranlogs.r-pkg.org/badges/grand-total/pliman) [![CRAN RStudio mirror downloads](https://cranlogs.r-pkg.org/badges/last-month/pliman?color=orange)](https://r-pkg.org/pkg/pliman) [![CRAN RStudio mirror downloads](https://cranlogs.r-pkg.org/badges/last-week/pliman?color=orange)](https://r-pkg.org/pkg/pliman) [![CRAN RStudio mirror downloads](https://cranlogs.r-pkg.org/badges/last-day/pliman?color=orange)](https://r-pkg.org/pkg/pliman) [![DOI](https://zenodo.org/badge/DOI/10.32614/CRAN.package.pliman.svg)](https://doi.org/10.32614/CRAN.package.pliman)



The pliman package offers tools for both single and batch image manipulation and analysis ([Olivoto, 2022](https://onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13803)), including the quantification of leaf area, disease severity assessment ([Olivoto et al., 2022](https://link.springer.com/article/10.1007/s40858-021-00487-5)), object counting, extraction of image indexes, shape measurement, object landmark identification, and Elliptical Fourier Analysis of object outlines, as detailed by Claude ([2008](https://link.springer.com/book/10.1007/978-0-387-77789-4)). The package also provides a comprehensive pipeline for generating shapefiles with complex layouts and supports high-throughput phenotyping of RGB, multispectral, and hyperspectral orthomosaics. This functionality facilitates field phenotyping using UAV- or satellite-based imagery.

`pliman` also provides useful functions for image transformation, binarization, segmentation, and resolution. Please visit the [Examples](https://nepem-ufsc.github.io/pliman/reference/index.html) page on the `pliman` website for detailed documentation of each function.

# Installation

Install the latest stable version of `pliman` from [CRAN](https://CRAN.R-project.org/package=pliman) with:

```{r, eval=FALSE}
install.packages("pliman")

```

The development version of `pliman` can be installed from [GitHub](https://github.com/nepem-ufsc/pliman) using the [pak](https://github.com/r-lib/pak) package:

```{r, eval=FALSE}
if(!requireNamespace("pak", quietly = TRUE)){
  install.packages("pak")
}
pak::pkg_install("nepem-ufsc/pliman")
```

*Note*: If you are a Windows user, you should also first download and install the latest version of [Rtools](https://cran.r-project.org/bin/windows/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](https://nepem-ufsc.github.io/pliman/articles/analyze_objects.html#adjusting-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).

```{r warning=FALSE, message=FALSE}
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

```

# 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).

```{r}
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
```

## 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.

```{r eval=FALSE}
img <- image_pliman("sev_leaf.jpg", plot = TRUE)

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

# Citation

```{r, comment=""}
citation("pliman")
```

# Getting help

If you come across any clear bugs while using the package, please consider filing a minimal reproducible example on [github](https://github.com/TiagoOlivoto/pliman/issues). 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](https://nepem-ufsc.github.io/pliman/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.

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