Provides tools for processing, analyzing, and visualizing spectral data 
    collected from 3D laser-based scanning systems. Supports applications in agriculture, 
    forestry, environmental monitoring, industrial quality control, and biomedical research. 
    Enables evaluation of plant growth, productivity, resource efficiency, disease 
    management, and pest monitoring. Includes statistical methods for extracting insights 
    from multispectral and hyperspectral data and generating publication-ready visualizations. 
    See Zieschank & Junker (2023) <doi:10.3389/fpls.2023.1141554> and 
    Saric et al. (2022) <doi:10.1016/J.TPLANTS.2021.12.003> for related work.

Large-scale phenotypic data processing is essential in research. Researchers need to eliminate outliers from the data in order to obtain true and reliable results. Best linear unbiased prediction (BLUP) is a standard method for estimating random effects of a mixed model. This method can be used to process phenotypic data under different conditions and is widely used in animal and plant breeding. The 'Phenotype' can remove outliers from phenotypic data and performs the best linear unbiased prediction (BLUP), help researchers quickly complete phenotypic data analysis. H.P.Piepho. (2008) <doi:10.1007/s10681-007-9449-8>.

Phenotype study cohorts in data mapped to the 
    Observational Medical Outcomes Partnership Common Data Model. Diagnostics 
    are run at the database, code list, cohort, and population level to assess 
    whether study cohorts are ready for research.

Methods to calculate and present 'PHENTHAUproc', an early warning and decision support system for hazard assessment and control of oak processionary moth (OPM) using local and spatial temperature data. It was created by Halbig et al. 2024 (<doi:10.1016/j.foreco.2023.121525>) at FVA (<https://www.fva-bw.de/en/homepage/>) Forest Research Institute Baden-Wuerttemberg, Germany and at BOKU - University of Natural Ressources and Life Sciences, Vienna, Austria.