LemnaTec Software for Phenotyping and Seed Testing
Translating images into application-specific information
The core of all LemnaTec phenotyping and seed testing systems is a comprehensive software package that enables operating the sensing equipment, storing the data and metadata, access to all records, and analysis of data. Hardware- and sensor- controls depend on the system and are specifically adapted for each product and also for customized solutions.
For all camera recordings, dedicated data processing is available. This ranges from systematic file administration over image processing to advanced machine learning algorithms. The image data processing and analysis is the key component of the phenotyping and seed testing procedures. Advanced image processing algorithms combined with machine learning tools ensure target-oriented analysis of the recorded image data. The analytical software translates digital image data into biologically relevant information.
Processing tools deliver sample-related data (examples):
|Sensor||Recorded parameters||Information to derive from parameters|
|Visible light camera||Reflectance in visible light spectrum|
|Counts, dimensions, texture, colour; growth and developmental features, stress responses|
(with corresponding excitation light and filter)
|Intensity and distribution of fluorescence light||Presence and distributions of fluorescencent pigments; stress and senescence|
|Camera for chlorophyll fluorescence dynamics (Kautsky/PAM)||Photosystem II-related parameters||Photosynthetic capacity and activity; stress, pathogen responses|
|NIR camera with 1450 nm filter||Water-content related NIR signal||Tissue moisture; water stress responses|
|IR camera||Surface heat emission||Plant temperatures, transpiration|
|Hyper-/multispectral camera||Spectrally resolved reflectance||Physiological parameters, vegetation indices|
|Laser scanner||Point clouds||3D surface, height map, inclination map, convex hull; growth and developmental features|
The parameters extracted from the camera recordings deliver phenotypic information. For this purpose, we deliver a toolbox phenotyping-related functions, e.g.:
Size, count, morphological parameters, and color of plants
Height and width
Plant organ specific parameters
Time course analyses, e.g. for growth rates
3D data of plants
Stress and pathogen responses, e.g. color changes, fluorescence changes
NIR reflectance as indicator for water content
Fluorescence intensity, e.g. biomarkers or chlorophyll
Chlorophyll fluorescence parameters as indicator of photosystem status and activity
Broad range of vegetation indices together with index calculator for customized indices – more than 60 pre-set vegetation indices available in the software package.
LemnaGrid offers access to LemnaTec customer specific machine learning based solutions.
LemnaTec software at work in plant- and seed- applications
Phenotyping of seedlings on petri dish
Assessing shoot and root traits of agar-grown seedlings with PhenoAIxpert: shoots and roots are recognized separately and measured for their individual size. In the current example, primary root length, secondary root length, and shoot area were measured for each seedling.
Artificial intelligence for seedling classification
Emerging seedlings consist of various parts, e.g. roots, shoots, or root hairs. With machine learning algorithms, SeedAIxpert can be trained to recognize each of these parts separately.
Arabidopsis growth assay
Trays with growing Arabidopsis plants were imaged with PhenoAIxpert and images were processed for plant growth, morphology and color. Individual plants were identified and phenotypic properties of their visible plant area were assessed. Such tests serve for e.g. candidate screening, genetic studies, treatment effects, environmental responses
Embryo and Endosperm are visible on the surface of maize seeds. Imaged and classified by SeedAIxpert, fractions of embryo and endosperm are measured for each seed.
Seedling emergence test
Oilseed rape germination was assessed in a seedling emergence test with seeds placed in substrate. Emerging cotyledons were recognized and measured for germination frequency and seedling quality.