Analyze PSII Signal

Extract Fv/Fm data of objects.

plantcv.fluor_fvfm(fdark, fmin, fmax, mask, bins=256)

returns Fv/Fm histogram headers, Fv/Fm histogram data, PSII analysis images (Fv image, Fv/Fm histogram)

  • Parameters:
    • fdark - image object, grayscale
    • fmin - image object grayscale
    • fmax - image object, grayscale
    • mask - binary mask of selected contours
    • bins - number of grayscale bins (0-256 for 8-bit images and 0 to 65,536), if you would like to bin data, you would alter this number (default bins=256)
  • Context:
    • Used to extract fv/fm per identified plant pixel.
    • Generates histogram of fv/fm data.
    • Generates fv/fm image.
  • Example use:

  • Output Data Units:

    • Bin-number - number of bins set by user
    • FV/FM Bins - bin values based on number of bins set by user
    • FV/FM Histogram - histogram of FV/FM ratio values for object
    • FV/FM Histogram Peak - bin value of histogram peak (greatest number of pixels)
    • FV/FM Median - bin value of histogram median
    • F-Dark Passed QC - Check (True or False) to determine if Fdark image does not have pixel intensity values above 2000.

Fdark image

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Fmin image

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Fmax image

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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 = "print"

# Analyze Fv/Fm    
fvfm_header, fvfm_data, fvfm_images = pcv.fluor_fvfm(fdark, fmin, fmax, kept_mask, 256)

# Store the two images
fv_img=fvfm_images[0]
fvfm_hist=fvfm_images[1]

# Pseudocolor the Fv/Fm image
pseudo_img = pcv.pseudocolor(gray_img=fv_img, mask=kept_mask)

Histogram of Fv/Fm values

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Pseudocolored output image based on Fv/Fm

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The grayscale Fv/Fm image (returned to analysis_image) can be used with the pcv.visualize.pseudocolor function which allows the user to pick a colormap for plotting.