  The beet cyst nematode (BCN) Heterodera schachtii is a plant pest responsible
for crop loss on a global scale. Here, we introduce a high-throughput system
based on computer vision that allows quantifying BCN infestation and
characterizing nematode cysts through phenotyping. After recording microscopic
images of soil extracts in a standardized setting, an instance segmentation
algorithm serves to detect nematode cysts in these samples. Going beyond fast
and precise cyst counting, the image-based approach enables quantification of
cyst density and phenotyping of morphological features of cysts under different
conditions, providing the basis for high-throughput applications in agriculture
and plant breeding research.
