Analyze a Hyperspectral Index

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

plantcv.hyperspectral.analyze_index(index_array, mask, bins=100, min_bin=0, max_bin=1)

returns None

  • Parameters:

    • index_array - instance of the Spectral_data class (created by running pcv.hyperspectral.extract_index)
    • mask - Binary mask made from selected contours
    • histplot - If True plots histogram of intensity values
    • bins - Optional, number of classes to divide spectrum 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.

  • 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 (example below). For more detail about data output see Summary of Output Observations

from plantcv import plantcv as pcv

pcv.hyperspectral.analyze_index(index_array=ndvi_index, mask=leaf_mask, histplot=True, bins=100, min_bin=0, max_bin="auto")

NDVI Index Image

Screenshot

Masked Index Histogram

Screenshot

Source Code: Here