Analyze a Spectral Index

This function calculates the spectral index statistics and writes the values as observations out to the Outputs class.

plantcv.analyze.spectral_index(index_img, labeled_mask, n_labels=1, bins=100, min_bin=0, max_bin=1, label=None)

returns None

  • Parameters:

    • index_img - instance of the Spectral_data class (created by running pcv.spectral_index)
    • labeled_mask - Labeled mask of objects (32-bit).
    • n_labels - Total number expected individual objects (default = 1).
    • bins - Optional, number of bins to divide the index values into (default bins=100)
    • min_bin - Optional, minimum bin label. Default of 0 will be used for the smallest bin label while calculating pixel frequency data unless otherwise defined. min_bin="auto" will set minimum bin to the smallest observed pixel value within the masked index provided.
    • max_bin - Optional, maximum bin label. Default of 1 will be used for the maximum bin label unless otherwise defined. max_bin="auto" will set maximum bin to the largest observed pixel value within the masked index provided.
    • label - Optional label parameter, modifies the variable name of observations recorded. Can be a prefix or list (default = pcv.params.sample_label).

  • Context:

    • Calculates data about mean, median, and standard deviation of an input index within a masked region.
    • If using an index that is expected to have negative values after masking (i.e. PRI) the default min_bin=0 will cut off pixel frequency data at 0 unless adjusted.
  • Example use:
    • Below
  • Output data stored: Mean, median, and standard deviation of the index automatically gets stored to the Outputs class when this function is ran. These data can always get accessed during a workflow. For more detail about data output see Summary of Output Observations

from plantcv import plantcv as pcv

# Set global debug behavior to None (default), "print" (to file), 
# or "plot" (Jupyter Notebooks or X11)

pcv.params.debug = "plot"
# Optionally, set a sample label name
pcv.params.sample_label = "plant"

pcv.analyze.spectral_index(index_img=ndvi_index, labeled_mask=mask,
                           min_bin=-1, max_bin=1)

NDVI Index Image

Screenshot

Masked Index Histogram

Screenshot

Source Code: Here