FLIP: FLuorescence Imaging Pipeline for field-based chlorophyll fluorescence images

Abstract

Photosynthesis is one of the most important biological reactions on earth providing oxygen and food for humanity. As global populations rise and arable land decreases, crops need to become more efficient at photosynthetic processes, particularly utilizing absorbed light energy. Chlorophyll fluorescence imaging is a rapid, non-destructive measurement that can provide information on the efficiency of the light-dependent reactions and carbon reactions of photosynthesis. Over the years chlorophyll fluorescence imaging systems have been developed and improved to capture two critical measurements: minimum fluorescence (F0) and maximum fluorescence (FM). These systems have primarily been utilized in controlled chamber or greenhouse settings focused at the single leaf or small plant level. To improve plant photosynthesis, fluorescence imaging data needs to be obtained from field-grown plants to capture canopy spatial effects. Previously developed software to extract F0and FMfrom controlled, leaf level images do not capture the complexity of the light-dependent reactions from field-grown plants. New software is needed that accounts for the canopy spatial effects from images of field-grown plants. FLIP: fluorescence imaging pipeline, was designed specifically for the TERR-REF field scanalyzer located at the University of Arizona’s Maricopa Agricultural Center located in Maricopa, Arizona but could be adapted for other field deployed fluorescence imaging systems. FLIP utilizes open source tools to convert binary images, apply a multi-threshold to extract the fluorescence data from the plant canopy, calculate photosynthetic efficiency, and assign those values to the appropriate experimental plot.

Keywords

• Chlorophyll fluorescence imaging
• Python
• ImageJ