Image-based LAI estimation with gap fraction theory

This repository provides data to develop and test leaf area index (LAI) estimation methods based on phenotyping images.
Crop: Soybean.

Corresponding code:
https://gitlab.ethz.ch/crop_phenotyping/image-based-lai-estimation-with-gap-fraction-theory

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).

Repository structure:

- 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