The Science of Plant Phenotype Analyses
Phenotypes are the most obvious but at the same time the hardest to quantify properties of plants, and broader speaking of any organism.
The term “phenotype” means the outer appearance, and it is the factor that serves for plant rating and classification since millennia. Via phenotypes, plants are assigned to groups and species, and phenotypes were the first selection criteria in any breeding process.
This early advantage turned into a difficulty as soon as biochemistry and genetics became available. In biochemical analyses, the target of analysis – e.g., DNA, protein, or lipid – is clearly defined and can be quantified in a standardized way. For phenotypes, there is no such clearly defined target of analysis, and this sets a challenge for any attempt of phenotypic measurement. Traditionally, phenotypes are mainly described semantically rather than numerically.
Phenotypic traits are influenced by both genetic and environmental factors and therefore, detailed phenotypic information is essential for genomic and physiological research. Nowadays, a multitude of mainly optical sensing methods is used to do the job in quantitative phenotyping, and traditional semantic descriptions of phenotypes are amended by digital data. Image acquisition and image processing have become key technologies in phenotyping. Beyond classical photographs, images in non-visible wavelengths, fluorescence images, spectral images, or laser scans can serve to measure phenotypic properties.
We at LemnaTec tackled this challenge to measure and quantify phenotypes with various imaging principles that match many application cases, combined with application-oriented image processing software. Using data extracted from digital images and scans, our systems deliver accurate phenotypic measurements of traits and provide insights into how development and environment can influence phenotypes and the way genes are expressed. To do so, our analytical software provides broad ranges of morphometric and physiology-related parameters that derive from the images.
Currently, our imaging and image processing is not only targeted at plants, but also at stress-causing plant-associated organisms (e.g. fungi, nematodes, caterpillars) or at foodand feeding materials.