PSII images (3 in a set; F0, Fmin, and Fmax) are captured directly following a saturating fluorescence pulse (red light; 630 nm). These three PSII images can be used to calculate Fv/Fm (efficiency of photosystem II) for each pixel of the plant. Unfortunately, our PSII imaging cabinet has a design flaw when capturing images of plants with vertical architecture. You can read more about how we validated this flaw using our PSII analysis workflows in the PlantCV Paper.
To run a PSII workflow over a single PSII image set (3 images) there are 4 required inputs:
# Import libraries
from plantcv import plantcv as pcv
class options:
def __init__(self):
self.image = "./img/PSII_PSD_supopt_temp_btx623_22_rep1.DAT"
self.debug = "plot"
self.writeimg= False
self.result = "psII_tutorial_results.json"
self.outdir = "." # Store the output to the current directory
# Get options
args = options()
# Set debug to the global parameter
pcv.params.debug = args.debug
# Read fluorescence image data
# Inputs:
# filename - Image file to be read in
# mode - How to read in the image; either 'native' (default), 'rgb', 'gray', or 'csv'
fdark1, fmin1, fmax1 = pcv.photosynthesis.read_cropreporter(args.image)
# Rotate each frame so that plant is upright
# Inputs:
# img - Image data
# rotation_deg - Rotation angle in degrees, can be a negative number, positive values move counter clockwise
# crop - If crop is set to True, image will be cropped to original image dimensions. If set to False, the image size will be adjusted to accommodate new image dimensions.
fdark = pcv.transform.rotate(img=fdark1, rotation_deg=-90, crop=False)
fmin = pcv.transform.rotate(img=fmin1, rotation_deg=-90, crop=False)
fmax = pcv.transform.rotate(img=fmax1, rotation_deg=-90, crop=False)