High-throughput Phenotyping of Nematode Cysts
This work addresses crop loss due to nematode pests for agriculture and plant breeding research, but it is incremental as it applies existing computer vision methods to a new agricultural domain.
The authors tackled the problem of quantifying beet cyst nematode infestation by developing a high-throughput computer vision system for detecting and phenotyping nematode cysts from microscopic soil images, enabling fast and precise cyst counting and morphological analysis.
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.