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README.Rmd
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README.Rmd
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---
always_allow_html: yes
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# pliman <img src="man/figures/logo.png" align="right" height="140/"/>
<!-- badges: start -->
[](https://CRAN.R-project.org/package=pliman) [](https://lifecycle.r-lib.org/articles/stages.html#stable)  [](https://r-pkg.org/pkg/pliman) [](https://r-pkg.org/pkg/pliman) [](https://r-pkg.org/pkg/pliman) [](https://zenodo.org/doi/10.5281/zenodo.4757333)
<!-- badges: end -->
The pliman (**pl**ant **im**age **an**alysis) package is designed to analyze plant images, particularly for leaf and seed analysis. It offers a range of functionalities to assist with various tasks such as measuring disease severity, counting lesions, obtaining lesion shapes, counting objects in an image, extracting object characteristics, performing Fourier Analysis, obtaining RGB values, extracting object coordinates and outlines, isolating objects, and plotting object measurements.
`pliman` also provides useful functions for image transformation, binarization, segmentation, and resolution. Please visit the [Examples](https://tiagoolivoto.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/TiagoOlivoto/pliman) using the [pak](https://github.com/r-lib/pak) package:
```{r, eval=FALSE}
pak::pkg_install("TiagoOlivoto/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://tiagoolivoto.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
# plot the density of the grain's length (in cm)
plot(flax, measure = "length")
```
# Analyzing orthomosaics
## Counting and measuring plants within plots
Here, I used `mosaic_analyze()` to count, measure, and extract image indexes at block, plot, and individual levels using an orthomosaic from a lettuce trial available in [this paper](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0274731). By using `segment_individuals = TRUE`, a deeper analysis is performed at individual levels, which enables counting and measuring the plants within plots. To reproduce, download the [lettuce mosaic](https://github.com/TiagoOlivoto/images/blob/master/pliman/lettuce/lettuce.tif?raw=true), and follow the tutorial below.
```{r eval = FALSE}
library(pliman)
set_wd_here() # set the directory to the file location
mo <- mosaic_input("lettuce.tif")
indexes <- c("NGRDI", "GLI", "SCI", "BI", "VARI", "EXG", "MGVRI")
# draw four blocks of 12 plots
an <- mosaic_analyze(mo,
r = 1, g = 2, b = 3,
nrow = 12,
segment_individuals = TRUE,
segment_index = "NGRDI",
plot_index = indexes)
```
## Canopy coverage and multispectral indexes
In this example, a multispectral orthomosaic originally available [here](https://github.com/diegojgris/draw-plots-qgis/blob/main/sampledata/MicaSenseMXRed_5bands.tif) is used to show how `mosaic_analyze()` can be used to compute the plot coverage and statistics such as min, mean, and max values of three multispectral indexes (NDVI, EVI, and NDRE) using a design that includes 6 rows and 15 plots per row. To reproduce, download the [orthomosaic](https://github.com/TiagoOlivoto/images/raw/master/pliman/NDSU/ndsu.tif?raw=true), save it within the current workind directory, and follow the tutorial below.
```{r eval=FALSE}
library(pliman)
set_wd_here() # set the directory to the file location
mosaic <- mosaic_input("ndsu.tif")
res <-
mosaic_analyze(mosaic,
nrow = 3, # use 6 if you want to analyze in a single block
ncol = 15,
buffer_row = -0.15,
buffer_col = -0.05,
segment_plot = TRUE,
segment_index = "NDVI",
plot_index = c("NDVI", "EVI", "NDRE"),
summarize_fun = c("min", "mean", "max"),
attribute = "coverage")
res$map_plot
```
## Counting and measuring distance betwen plants
In this example, an RGB orthomosaic from a rice field originally available [here](https://github.com/aipal-nchu/RiceSeedlingDataset) is used to show how `mosaic_analyze()` can be used to count plants and measure the distance between plants within each plot. The first step is to build the plots. By default a grid (`grid = TRUE`) is build according to the `nrow` and `ncol` arguments. In this step, use the "Drawn polygon" button to drawn a polygon that defines the area to be analyzed. After drawing the polygon, click "Done". When the argument `check_shapefile = TRUE` (default) is used, users can check if the plots were correctly drawn. In this step, it is also possible to a live edition of the plots by clicking on "edit layers" button. After the changes are made, don't forget to click "Save". To remove any plot, just click on "Delete layers" button, followed by "Save". After all the editions are made, click "Done". The function will follow the mosaic analysis using the edited shapefile. After the mosaic has been analyzed, a plot is produced by default. In this plot, individuals are highlighted with a color scale showing the area of each individual. The results on both plot- and individual level are stored in data frames that can be easily exported for further analysis
To reproduce, download the [rice_ex.tif mosaic](https://github.com/TiagoOlivoto/images/raw/master/pliman/rice_field/rice_ex.tif?raw=true), save it within the current working directory, and follow the tutorial below.
```{r eval=FALSE}
library(pliman)
set_wd_here() # set the directory to the file location
mosaic <- mosaic_input("rice_ex.tif")
res <-
mosaic_analyze(mosaic,
r = 1, g = 2, b = 3,
segment_individuals = TRUE,
segment_index = "(G-B)/(G+B-R)",
filter = 4,
nrow = 8,
map_individuals = TRUE)
```
### Using an external shapefile
When a shapefile is provided there is no need to build the plots, since the function will analyze the mosaic assuming the geometries provided by the shapefile. To reproduce, download the [mosaic](https://github.com/TiagoOlivoto/images/raw/master/pliman/rice_field/rice_ex.tif?raw=true) and [shapefile](https://github.com/TiagoOlivoto/images/raw/master/pliman/rice_field/rice_ex_shp.rds?raw=true) needed, save them within the current working directory and follow the scripts below.
```{r eval=FALSE}
library(pliman)
set_wd_here() # set the directory to the file location
# Import the mosaic
mosaic <- mosaic_input("rice_ex.tif")
# Import the shapefile
shapefile <- shapefile_input("rice_ex_shp.rds")
# analyze the mosaic using the shapefile
res <-
mosaic_analyze(mosaic,
r = 1, g = 2, b = 3,
shapefile = shapefile,
segment_individuals = TRUE,
segment_index = "(G-B)/(G+B-R)",
filter = 4,
map_individuals = TRUE)
# Distances between individuals within each plot
str(res$result_individ_map)
# plot-level results
str(res$result_plot_summ)
# individua-level results
str(res$result_indiv)
```
# 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://tiagoolivoto.github.io/pliman/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.