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