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This repository provides code examples to estimate leaf area index (LAI) based on phenotyping images.
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**Manuscript title**: Extracting leaf area index using viewing geometry effects—A new perspective on high-resolution unmanned aerial system photography
**Manuscript details**:
Roth, Lukas, Aasen, Helge, Walter, Achim, and Frank Liebisch. 2018. Extracting leaf area index using viewing geometry effects—A new perspective on high-resolution unmanned aerial system photography. ISPRS Journal of Photogrammetry and Remote Sensing.
(https://doi.org/10.1016/j.isprsjprs.2018.04.012).
## Quick start
See `Simple_Example.R` for a quick example code how to estimate LAI based on multi-view data.
## Repository structure
**Data**
- ground_truth: Manual measurements of LAI
- *_plots.csv: Experimental design with genotype names
- *_plants_per_m.csv: Manually counted plants per meter
- *_true_LAI.csv: LAI determined by imaging leaves with image station
- *_LAI_meter.csv: LiCor LAI-2200 measurements
- *_biomass.csv: Measured dry biomass
- *_gravimentric_LAI.csv: Estimated LAI based on relation biomass and LAI
- remote_sensing: Projected visible leaf area based on drone images
- PA_p.csv: Projected leaf area per image
- viewpoint.csv: Viewpoint information for images (e.g., azimuth and zenith angle)
- simulation: Simulated data
- CC.csv: Projected leaf area per simulated image
- camera_position_all.csv: Simulated viewpoint information
**Code**
- `Simple_Example.R`: Example code to estimate LAI based on multi-view data
- `Full_Analysis.R`: Full analysis of the data according to published paper
- subscripts: Folder with auxiliary R scripts
**Software requirement**:
- Operation system: Any
- R version: 4.1.1