title: “VPR_processing” output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{VPR_processing} %\VignetteEngine{knitr::knitr} \usepackage[utf8]{inputenc}

author: “Emily Chisholm (https://orcid.org/0000-0002-1072-5879), Kevin Sorochan, Catherine Johnson”


Section 1: Background

This document was produced at Bedford Institute of Oceanography (BIO) to accompany the vprr package, a processing and visualization package for data obtained from the Video Plankton Recorder (VPR) produced by SeaScan Inc. The VPR consists of a CPU, CTD, and camera system with different optical settings (i.e., magnifications). It captures underwater images and records their corresponding salinity, temperature, and depth. The vprr package functions to join environmental and plankton data derived from the CTD and camera, respectively, and calculate plankton concentration and averaged environmental variables along the path of the VPR. This document refers to processing of data from the 2008 version of the “Digital AutoVPR” model.The data processing tools in vprr should be adaptable to more recent VPR models from SeaScan Inc.

The VPR outputs two raw files (.dat and .idx) for a given time period in a deployment. These files are processed together in a software provided with the VPR (i.e., Autodeck), which decompresses the images, extracts “regions of interest” (ROIs), and outputs ROI image files and a corresponding CTD data file (.dat). The ROI file names are numeric consisting of 10 digits. The first 8 digits correspond to the number of milliseconds elapsed in the day at the time the image was captured. The last two digits correspond to the ROI identifier (01-99). The ROIs and corresponding CTD data are linked by their 8 digit time stamp. After the ROIs have been extracted from the raw files they may be sorted into categories manually or by an automated classification procedure. At BIO, the images were sorted using a software called “Visual Plankton” (VP) in Matlab, which was developed specifically for the VPR. A version of VP modified by personnel at BIO, primarily to update and utilize the Support Vector Machine (SVM) image classification component, can be found on GC code within the dfo-mar-odis group, under the visual plankton project in the Matlab directory. For more information or permission to view the linked project on GC code, contact DataServicesDonnees@dfo-mpo.gc.ca.

Visual Plankton outputs two text files (“aid” and “aidmeas” files) for each category of classification. Each “aid” (i.e., autoid) file contains file paths to individual ROIs that have been classified to the category of interest. The corresponding “aidmeas” file contains morphological data for the ROIs (e.g., long axis length, perimeter, etc.). The aid, aidmeas, and CTD files are the data inputs for processing using vprr. Note that the functionality of vprr is dependent on the format and directory structure of the data output from VP, but is not dependent on the use of VP itself. The primary functions of the vprr package are to manually correct misclassifications made by VP, join CTD and aid or aidmeas datasets, and compute plankton concentrations. Example code for visualization of final data products are provided in this vignette.

Figure 1. VPR data processing flow chart. Blue boxes represent software, green and yellow boxes represent data sets, where yellow is visual data and green is text format data. This document outlines the processes within 'Processing and Visualization (R).

Section 2: Summary of vprr data processing steps

The processing steps in R can be sub divided from the 'Post Processing and Visualization box in Figure 1. Figure 2. Processing steps in R using vprr. Blue boxes represent each section of processing, green ovals represent sub steps or routines within each step.

Section 2.1: Processing Environment

Before beginning data processing with vprr, it is recommended that a processing environment be created containing commonly used variables and file paths. The simplest and most reproducible way to achieve this is to write an R script where all the mission and system specific variables are contained, then save the environment as a RData file to be loaded at the start of any processing scripts. This processing environment contains reference to a station identifier csv file which should be created for each mission. This file links station names from deck sheets to the day and hour values on which Autodeck organizes files. Day and hour values represent the Julian day (3 digit) and two digit hour (24 hour clock) when sampling was done. Note that the day and hour values will be in the time zone of the computer used to run Autodeck. Ensure that this matches the time zone of the VPR CPU at the time of data collection to avoid a time offset between data sources.

Another important part of setting up the processing environment is ensuring the proper directory structure is in place, see Appendix 1 for details on the required directory structure.

# set VPR processing environment


cruise <- 'COR2019002'

year <- 2019

station_names_file <- paste0("station_names_", cruise, ".csv")
# example: 'C:/VPR_PROJECT/vp_info/station_names_COR2019002.csv'
# note columns should be labeled : station, day, hour

# DIRECTORY FOR CTD DATA (output from AutoDeck)
castdir <- paste0('D:/', cruise, "/", cruise, "_autodeck/")
# example: 'D:/COR2019002/COR2019002_autodeck/'

drive <- 'C:/'
auto_id_folder <- paste0(drive, "cruise_", cruise, "/", autoid)
# example: 'E:/COR2019002/autoid' #!!NO BACKSLASH AT END OF STRING

auto_id_path <- list.files(paste0(auto_id_folder, "/"), full.names = T) 

savedir <- paste0(cruise, '_data_files')
dir.create(savedir, showWarnings = FALSE)

stdir <- paste('data_product/', cruise, sep = "")
dir.create(stdir, showWarnings = FALSE, recursive = TRUE)

binSize <- 3

#### SAVE ####

save.image(file = paste0(cruise,'_env.RData'))

An example of the station names csv file looks like this:


Once this environment is set, it can be loaded into any processing session by using

load('COR2019002_env.RData') # where COR2019002 is mission name

If sharing processing code with colleagues on version control, keeping the environment variables separate (outside of the git project) will allow collaboration while avoiding inconsistencies in file paths or folder names.

Section 2.2: Image Copying (optional):

ROIs are organized into folders corresponding to their assigned classification categories from VP. The information in each aid file produced by VP is used to create a folder for each classification category containing ROIs that have been classified to that category. This step is only required if manual re-classification (see Section 2.2) is intended. Further details on image copying are provided in Section 3.

Section 2.3: Manual re-classification (optional):

Classifications from VP are manually checked, which allows for manual correction and addition of categories not previously used for classification in VP. A user-defined subset of the ROIs that have been copied in Section 2.1 is manually sorted to correct for misclassifications made by VP. Updated aid and aidmeas files are produced. Further details on manual re-classification are provided in Section 4.

Section 2.4: Processing:

Joining data outputs from Autodeck (ctd .dat files) and VP (aid and aidmeas files). The aid and aidmeas files, which may have been updated (see Section 2.2) are joined with CTD text files by the 8 digit time stamp. The data are then averaged in user-defined vertical bins to produce a time series of plankton concentrations and environmental variables. Quality controlled data products (before and after binning) are then exported in simple formats (csv, RData, oce) for plotting and analysis. Further details on data processing are provided in Section 5.

Section 3: Image Copying

In this step, ROIs are copied to folders that are organized based on the day and hour of data collection and classification category assigned from VP. The images are organized by Autodeck into day and hour; however, reorganizing them based on classification allows easier human interaction with the data and visual inspection of classifications by VP. Moreover, this directory structure is used by the next step of processing (i.e., manual re-classification). To implement this step use the function vprr::vpr_autoid_copy(). For more information on input variables, please see documentation for vpr_autoid_copy (?vpr_autoid_copy).

# create variables
# ---------------------
basepath <- "C:\\data\\cruise_COR2019002\\autoid\\" 
# note this is the same as the auto_id_folder environment variable except the file separator is different, because this script will run source code in command line which does not recognize '/' as a file separator

day <- "123"
hour <- "01" # note leading zero 
classifier_type <- "svm" 
classifier_name <- "myclassifier"

# run file organizer
# ---------------------
vpr_autoid_copy(basepath, day, hour, classifer_type, classifier_name)

Section 4: Manual Re-classification

Manual re-classification of some categories after automated classification by VP may be required to achieve identification accuracy standards. In this step, ROIs are displayed on the screen one at a time for manual verification. If VP has misclassified an image or if it falls into a new user defined category (described below), the image can be re-classified. At the end of this process, text files which match the formats produced by VP for aid and aidmeas files are generated with the new classifications. The manual re-classification process also allows for creation of new categories. This is especially useful for classification of rare categories that were not defined prior to classification in VP. For example, if VP produces a classification group “copepods” containing species A and species B, the user can add “species A” and “species B” categories in the manual re-classification step using the function vprr::vpr_category_create (see example below). After completing manual re-classification for a day-hour set, new aid and aidmeas files are created for new categories, which are identical in format to original aid and aidmeas files produced by VP.

Section 4.1: Preparing the environment by setting some variables

# -------------------------------------

# Once classified images are sorted by taxa
# verify classification accuracy by manually 
# looking through classified images

#### USER INPUT REQUIRED ####     


day <- '235' 
hr <- '19' # keep leading zeros, must be two characters

category_of_interest <-
    'speciesA', # new category
    'speciesB' # new category

# add new category (optional)

vpr_category_create(taxa = taxa_of_interest, auto_id_folder)
# ensures there is proper folder structure for all categories of interest

# reclassify images
vpr_manual_classification(day = day, hour= hr, basepath = auto_id_folder,gr = FALSE, 
          taxa_of_interest = category_of_interest, scale = 'x300',
          opticalSetting = 'S3')

Section 4.2: Generate new aid and aidmeas files

The function vprr::vpr_manual_classification() produces two files (‘misclassified’ and ‘re-classified’ text files) as a record of manual re-classification, which are found in the R project working directory in folders named by the day and hour that the data were collected. The function vprr::vpr_autoid_create() takes these files and outputs new aid and aidmeas files in the R working directory in folders named by classification category. This step should be run after each hour of data is manually re-classified.

# -----------------------------------------

day_hour_files <-  paste0('d', day, '.h', hr)

misclassified <- list.files(day_hour_files, pattern = 'misclassified_', full.names = TRUE)

reclassify <- list.files(day_hour_files, pattern = 'reclassify_', full.names = TRUE)

vpr_autoid_create(reclassify, misclassified, auto_id_folder)

Section 4.3: File check

The last step of manual re-classification includes some manual file organization and final checks. These files should be manually reorganized in a new directory which will become the new auto_id_folder (see Appendix 1: Directory Structure). Remember that aid and aidmeas files from any categories which were not manually checked and re-classified should also be added to this new auto_id_folder. After the updated aid and aidmeas files have been manually reorganized they should be quality controlled using vprr::vpr_autoid_check(). This function removes any empty aid files created, if there are no images of a specific classification group in a particular hour of VPR deployment, which can cause errors in processing down the line. This function also checks that (1) aid and aidmeas files are matching within an hour of data; (2) aid and aidmeas files include the same number of ROIs; and (3) the VPR tow number for all files is the same.

# --------------------------------

# aid check step
# removes empty aid files, and checks for errors in writing

vpr_autoid_check(basepath, cruise) #OUTPUT: text log 'CRUISE_aid_file_check.txt’ in working directory

Section 5: Data Processing

This is the main chunk of coding required to generate data products. This step does not require image Copying (Section 3) or manual re-classification (Section 4) steps. The following is a walk-through of processing data from a DFO field mission (i.e. mission COR2019002) in the southern GSL (Gulf of St. Lawrence) in 2019 First, all libraries should be loaded and the processing environment, described in Section 2.4 should be loaded.

##### PROCESSING  --------------------------------------------------------------------------------------------------------------------

#### FILE PATHS & SETTINGS --------------------------------------------------------------------------------------------------------------------
# loads processing environment specific to user


This section allows a user to process all stations of a particular mission in a loop. This can be modified or removed based on personal preference

##### STATION LOOP ----------------------------------------------------------------------------------------------------------------------------

all_stations <- read.csv(station_names_file, stringsAsFactors = FALSE) 
all_stations_of_interest <- unique(all_stations$station) 

for (j in 1:length(all_stations_of_interest)){

  station_of_interest <- all_stations_of_interest[j] 

  cat('Station', station_of_interest, 'processing... \n')

Optical settings and image volume variables should be set. If they are consistent throughout the mission, they could also be added to the processing environment (Section 2.4).

  #   Set optical settings  & Image Volume   #
  #   !Should be updated with each mission!   #
  if(cruise == "IML2018051") {

    opticalSetting <- "S2"
    imageVolume <- 108155 #mm^3

CTD casts are loaded in using vprr::vpr_ctd_files to find files and vprr::vpr_ctd_read to read in files. During CTD data read in, a standardized seawater density variable sigmaT is derived using the function oce::swSigmaT, and depth (in meters) is derived using the function oce::swDepth.

  #get day and hour info from station names list
  dayhour <- vpr_dayhour(station_of_interest, file = station_names_file)

  ##### PULL CTD CASTS ----------------------------------------------------------------------------------------------------------------------------
  # get file path for ctd data

  # list ctd files for desired day.hours
  ctd_files <- vpr_ctd_files(castdir, cruise, dayhour) 

  ##### READ CTD DATA ----------------------------------------------------------------------------------------------------------------------------

  ctd_dat_combine <- vpr_ctd_read(ctd_files, station_of_interest)

  cat('CTD data read complete! \n')

VPR data files are then found, within the VP directory structure.

  ##### FIND VPR DATA FILES ----------------------------------------------------------------------------------------------------------------------

  # Path to aid for each taxa                               
  aid_path <- paste0(auto_id_path, '/aid/')              
  # Path to mea for each taxa                               
  aidmea_path <- paste0(auto_id_path, '/aidmea/')        

  aid_file_list <- list()
  aidmea_file_list <- list()
  for (i in 1:length(dayhour)) {
    aid_file_list[[i]] <-
      list.files(aid_path, pattern = dayhour[[i]], full.names = TRUE)
    aidmea_file_list[[i]] <-
      list.files(aidmea_path, pattern = dayhour[[i]], full.names = TRUE)

  aid_file_list_all <- unlist(aid_file_list)
  aidmea_file_list_all <- unlist(aidmea_file_list)

  remove(aid_file_list, aidmea_file_list, aid_path, aidmea_path)

ROI and measurement data files are then read using vprr::vpr_autoid_read.

  ##### READ ROI AND MEASUREMENT DATA ------------------------------------------------------------------------------------------------------------

  # ROIs
  roi_dat_combine <-
      file_list_aid = aid_file_list_all,
      file_list_aidmeas = aidmea_file_list_all,
      export = 'aid',
      station_of_interest = station_of_interest,
      opticalSetting = opticalSetting

  roimeas_dat_combine <-
      file_list_aid = aid_file_list_all,
      file_list_aidmeas = aidmea_file_list_all,
      export = 'aidmeas',
      station_of_interest = station_of_interest,
      opticalSetting = opticalSetting

  cat('ROI and measurement data read in complete! \n')

Next, CTD and aid data are merged to create a data frame describing both environmental variables (eg. temperature, salinity) and classified images. The function used is vprr::vpr_ctdroi_merge.

  ##### MERGE CTD AND ROI DATA ---------------------------------------------------------------------------------------------------------------------
  ctd_roi_merge <- vpr_ctdroi_merge(ctd_dat_combine, roi_dat_combine)

  cat('CTD and ROI data combined! \n')

Before final export of data products, the following variables are added to the data frame: time in hours avg_hr is calculated, and a time stamp (ymdhms) with POSIXct signature in Y-M-D h:m:s format is added using the function vpr_ctd_ymd.

  ##### CALCULATED VARS ----------------------------------------------------------------------------------------------------------------------------

  # add avg hr and sigma T data and depth
 data <- ctd_roi_merge %>%
    dplyr::mutate(., avg_hr = time_ms / 3.6e+06) 

    data <- vpr_ctd_ymd(data, year)

  cat('Initial processing complete! \n')

  # clean environment

Average plankton concentration and environmental variables (e.g., temperature, salinity, density, etc.) are then computed within a user defined depth bin. The bin-averaging step standardizes plankton concentrations when the VPR does not sample the water column evenly, due to characteristics of the deployment or variability in the sampling rate, which is not necessarily constant in older versions of the VPR, and reduces noise in the data. First, an oce CTD object is created using vprr::vpr_oce_create. Then, bin-averaging is done using vprr::bin_cast. Concentrations are calculated for each category of interest.

  ##### BIN DATA AND DERIVE CONCENTRATION ----------------------------------------------------------------------------------------------------------

  ctd_roi_oce <- vpr_oce_create(data)

# bin and calculate concentration for all taxa (combined)
  vpr_depth_bin <- bin_cast(ctd_roi_oce = ctd_roi_oce, binSize =  binSize, imageVolume = imageVolume)

  # get list of valid taxa
  taxas_list <- unique(roimeas_dat_combine$taxa)

  # bin and calculate concentrations for each category
  taxa_conc_n <- vpr_roi_concentration(data, taxas_list, station_of_interest, binSize, imageVolume)  

  cat('Station', station_of_interest, 'processing complete! \n')

  # bin size data

  size_df_f <- vpr_ctdroisize_merge(data, measdata = roimeas_dat_combine, taxa_of_interest = category_of_interest)

  size_df_b <- vpr_size_bin(size_df_f, bin_mea = 3)

Finally, data are saved as RData and csv files for export and plotting. Data are also saved as an oce object in order to preserve both data and metadata in an efficient format.

  ##### SAVE DATA ---------------------------------------------------------------------------------------------------------------------------------
  # Save oce object
  oce_dat <- vpr_save(taxa_conc_n)
  save(file = paste0(savedir, '/oceData_', station_of_interest,'.RData'), oce_dat) # oce data and metadata object

  # Save RData files
  save(file = paste0(savedir, '/ctdData_', station_of_interest,'.RData'), ctd_dat_combine) #CTD data
  save(file = paste0(savedir, '/stationData_', station_of_interest,'.RData'), data) # VPR and CTD data
  save(file = paste0(savedir, '/meas_dat_', station_of_interest,'.RData'), roimeas_dat_combine) #measurement data
  save(file = paste0(savedir, '/bin_dat_', station_of_interest,'.RData'), vpr_depth_bin) # binned data with cumulative concentrations
  save(file = paste0(savedir, '/bin_size_dat_', station_of_interest,'.RData'), size_df_b) # binned data inclouded measurements

  cat('CTD, ROI-VPR merge, ROI measurement saved as RData! \n')

  # Write csv files
  # write.csv(file = paste0(stdir, '/vpr_data_unbinned', station_of_interest, '.csv'), data, row.names = F) # VPR and CTD data
  # write.csv(file = paste0(stdir, '/vpr_meas', station_of_interest, '.csv'), roimeas_dat_combine) # measurement data
  write.csv(file = paste0(stdir, '/vpr_data_binned', station_of_interest, '.csv'), taxa_conc_n) # VPR and CTD data with concentrations by taxa

  cat('ROI measurments, ROI-CTD merge-unbinned, and ROI-CTD merge-binned written to csv! \n')

} #end of station loop

Section 6: Plotting

Although not primarily a plotting package, vprr can produce contour plots, profile plots and temperature-salinity (TS) plots from VPR data sets. A few example plots are provided in the following code. The first step to plotting is properly loading in the processed VPR data objects developed in processing. The environment, described in Section 2.4 should also be loaded. The individual data files are found by distinct names (e.g., “stationData”). The directory structure may be different depending on the savedir where data files were saved during processing. Note that the following plotting examples are tailored for tow-yo pattern VPR deployments.

##### FILE PATH & SETTINGS -----------------------------------------------------------------------------------------------------------------------


# loads all file paths and environment vars specific to User

#find all data files
fn_all_st <- list.files(paste0(cruise, "_data_files/"), pattern = "stationData", full.names = T)
fn_all_meas <- list.files(paste0(cruise, "_data_files/"), pattern = "meas", full.names = T)
fn_all_conc <- list.files(paste0("data_product/", cruise, "/"), pattern = "data_binned", full.names = T)
fn_all_bin <- list.files(paste0(cruise,"_data_files/"), pattern = 'bin_dat', full.names = T)

Once files are loaded, plots for all stations in a mission can be generated using a loop, in order to efficiently generate comparable plots. The example below uses a loop to run through a list of stations described by a csv file. This loop also isolates two specific classification categories to plot (eg. “Calanus” and “krill”).

####START STATION LOOP ---------------------------------------------------------------------------------------------------------------------------


all_stations <- read.csv(station_names_file, stringsAsFactors = FALSE)
all_stations_of_interest <- unique(all_stations$station)

taxa_to_plot <- c("Calanus", "krill")

for (j in 1:length(all_stations_of_interest)){

  station <- all_stations_of_interest[j]

  cat('station', station ,'starting to plot.... \n')

Data files are loaded for the specific station of interest. This loads in all relevant RData files as well as the concentration data saved as a csv file.

  #load station roi and ctd data
  fn_st <- grep(fn_all_st, pattern = station, value = TRUE, ignore.case = TRUE)
  fn_meas <- grep(fn_all_meas, pattern = station, value = TRUE, ignore.case = TRUE)
  fn_conc <- grep(fn_all_conc, pattern = station, value = TRUE, ignore.case = TRUE)
  fn_bin <- grep(fn_all_bin, pattern = station, value = TRUE, ignore.case = TRUE)


 # load concentration data
  taxa_conc_n <- read.csv(fn_conc, stringsAsFactors = F)

  station_name <- paste('Station ', station)

The final section of set up indicates the directory in which plots will be saved and provides generic plot size arguments which will control how large the saved .png files are.

  # directory for plots
  stdir <- paste0('figures/', cruise, '/station', station)
  dir.create(stdir, showWarnings = FALSE, recursive = TRUE)

  width = 1200
  height = 1000

The following example presents a plot of the concentrations of a taxon as scaled bubbles along the tow path, overlain on contours of an environmental variable from the CTD. The main function used is vprr::vpr_plot_contour which uses a standard VPR data frame (taxa_conc_n - produced from processing (Section 5)) and plots the background contours. Interpolation methods can be adjusted based on data or preference. The VPR tow path can be added on top of contours, with concentration data displayed as scaled bubbles. This method can be repeated with various environmental variables (e.g., temperature, salinity etc.) used to calculate the contours, by changing the var argument in vprr::vpr_plot_contour.

  # Density (sigmaT)
  png('conPlot_taxa_dens.png', width = width, height = height)
  p <- vpr_plot_contour(taxa_conc_n[taxa_conc_n$taxa %in% c(taxa_to_plot),], var = 'density', dup = 'strip', method = 'oce', bw = 0.5)
  p <- p + geom_line(data = data, aes(x = avg_hr - min(avg_hr), y = pressure), col = 'snow4', inherit.aes = FALSE) +
    geom_point(data = taxa_conc_n[taxa_conc_n$taxa %in% c(taxa_to_plot),], aes(x = avg_hr, y = min_pressure, size = conc_m3), alpha = 0.5)+
    ggtitle(station_name ) +
    labs(size = expression("Concentration /m" ^3), fill = 'Density')+
    scale_size_continuous(range = c(0, 10)) +
    facet_wrap(~taxa, ncol = 1, scales = 'free') +
    theme(legend.key.size = unit(0.8, 'cm'),
          axis.title = element_text(size = 20),
          strip.text = element_text(size = 20),
          plot.title = element_text(size = 32), 
          axis.ticks = element_line(size = 1, lineend = 'square'),
          axis.text = element_text(size = 30),
          legend.text = element_text(size = 20),
          legend.title = element_text(size = 25)


Vertical profiles of plankton concentration and environmental variables compressed over the sampling duration can be generated using vprr::vpr_plot_profile. This type of plot indicates the overall pattern in vertical distribution over the VPR deployment.

    png('profilePlots_RK.png', width = 1000, height = 500)
    p <- vpr_plot_profile(taxa_conc_n, taxa_to_plot)

Temperature-salinity (TS) plots can be generated to visualize how plankton concentration varies across different water masses. In the example below, a TS plot is produced in ggplot (with labeled isopycnals), and concentration bubbles for each selected classification group are overlaid on the plot. The basic TS bubble plot can be easily manipulated using ggplot2 grammar, for example the plots can be faceted by classification group or axis labels and sizing can be adjusted.

####TS BUBBLE PLOT ----------------------------------------------------------------------------------------------------------------------------

  # plot by taxa
  taxa_conc <- taxa_conc_n[taxa_conc_n$conc_m3 > 0,]
  png('TS_conc_taxa.png', width = 1000, height = 500)
  p <- vpr_plot_TS(taxa_conc[taxa_conc$taxa %in% c(taxa_to_plot),], var = 'conc_m3') +
    facet_wrap(~taxa, nrow = 1) +
    theme(strip.text = element_text(size = 18),
          axis.title = element_text(size = 20),
          panel.spacing = unit(2, 'lines'))

  cat('station', station, 'complete! \n')

} # end station loop

Section 7: Disclaimer

The functions in vprr were created for a specific project and have not been tested on a broad range of field mission data. It is possible that deviations in data format and directory structure from that described herein may result in errors when using vprr. The vprr package was developed for the purpose of processing data collected during tow-yo VPR deployments and image classification using VP. The purpose of this document is to provide a template for processing and visualizing VPR data that can be adapted by other users for their own objectives.

Appendix 1: Directory Structure

Visual Plankton (Matlab image classification software) requires a very specific directory structure in order to function. Since this processing is meant to directly follow this image classification, the VP directory structure is used for consistency. This allows a smooth transition between the Matlab classifications and the completion of processing in R. The directory structure required is described below

Appendix 2: Glossary

Aid files

AidMeas files (AutoID measurements)

Auto Deck

Auto ID

AutoID files


Classification category (Taxa)





Image volume

Optical Setting









Working Directory