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High thoroughput Plant phenotyping : Light interception measurement
Anjana K N , Dr. Stephen Nuske | Field Robotics Centre, Robotics Institute, Carnegie Mellon University
Tools used
• Ueye camera ( IDS-UI1246LE-C-HQ )
• Pointgrey Camera (Flea3 GigE)
• Fish eye lenses
• ROS, MATLAB, OpenCV
Experiment:
• Fish eye lens attached to the upward facing monocular camera, positioned 6 inches above the ground
Thanks to Stephen Nuske for guidance and supporting this work , Omeed Mirbod and ZaniaPothen for help
A special thanks to Rachel Burcin, Mikana Maeda and the RISS team
High-throughput image-based phenotyping is a technology that can image hundreds of plants per day with minimal time and effort
Plant traits are captured non-destructively
To collect high-precision, high-resolution measurements of plants in field settings using distributed sensor networks
Calculation of Leaf Area Index and proportionate leaf area estimation in various heights and radii
INTRODUCTION
Fig.1: Image of the sorghum field with the phenotyping system Validating the results with data from light
measurement meters and other techniques
Capturing more plant traits
Extending the technique to other crop varieties
Leaf area and Leaf Area Index (LAI) calculated using camera calibration parameters
LAI= Total one‐sided area of leaf tissueper unit ground surface area
Leaf area is found at various radii with respect to the camera center for light interception calculation
• Collected images subjected to binary thresholding for image segmentation
Fig.2: Captured images and segmented images
METHODS
RESULTS
FUTURE WORK
ACKNOWLEDGEMENT
Map of the field generated from the data using GPS coordinates
OBJECTIVES
Fig.4: Field map with color bar
The processed data can then be used to quantify genotype by phenotype and by environment interaction
Fig.3: Proportional leaf area in various radii