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arXiv · 2110.07057

High-throughput Phenotyping of Nematode Cysts

Long Chen·Matthias Daub·Hans-Georg Luigs·Marcus Jansen·Martin Strauch·Dorit Merhof
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Abstract

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.

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Long Chen, Matthias Daub, Hans-Georg Luigs, Marcus Jansen, Martin Strauch, Dorit Merhof. 2021-10-13. High-throughput Phenotyping of Nematode Cysts. https://arxiv.org/abs/2110.07057

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