In an increasingly digitised and data-driven world, there is a pressing
need for globally reproducible high-throughput morphological
phenotyping, which provides quantitative and objective data and markers
of seed quality to guide analysis and research. Current lab-based
methods for morphological phenotyping of germinating seeds still largely
rely on visual or 2D-imaging technologies with their respective
limitations. Here we present the phenoTest, a novel high-throughput
3D-phenotyping technology that alleviates many of the drawbacks of
conventional testing and research methods. Using Xray, 3D-volume image
data of individual seedlings grown under highly-standardized conditions
are captured. Through an AI-based algorithm, all plant organs can be
automatically segmented and measured in 3D, currently outputting 50
seedling datasets per 2 minutes. Individual seedlings can be traced over
time across the developmental process without disrupting the germination
containers. The process can be run in fully-automated, 24h operation and
is industrially validated for multiple years. The phenoTest is
universally applicable with customized algorithms for all plants and
crops, covering the entire range from fine grasses, vegetables to
forestry seeds. The process enables a quantitative, objective and
reproducible assessment of morphological seedling traits in 4D which can
substitute visual gemination and vigor testing and can be harnessed to
optimise processing and breeding. We will share data on the effects of a
multitude of factors such as seed treatment, ageing, storage and
packaging on the speed and quality of seedling development, and the
homogeneity, degree of abnormalities, germination capacity and vigor of
seed lots of different crop types.