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The beet cyst nematode (BCN) Heterodera schachtii is a plant pest responsiblefor crop loss on a global scale. Here, we introduce a high-throughput systembased on computer vision that allows quantifying BCN infestation andcharacterizing nematode cysts through phenotyping. After recording microscopicimages of soil extracts in a standardized setting, an instance segmentationalgorithm serves to detect nematode cysts in these samples. Going beyond fastand precise cyst counting, the image-based approach enables quantification ofcyst density and phenotyping of morphological features of cysts under differentconditions, providing the basis for high-throughput applications in agricultureand plant breeding research.

High-throughput Phenotyping of Nematode Cysts