High-throughput phenotyping of nematode cysts - Chen, Long; Daub, Matthias; Luigs, Hans-Georg; Jansen, Marcus; Strauch, Martin; Merhof, Dorit (Frontiers in Plant Science, 2022)
The beet cyst nematode 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 beet cyst nematode infestation and measuring phenotypic traits of cysts. After recording microscopic images of soil sample extracts in a standardized setting, an instance segmentation algorithm serves to detect nematode cysts in these images. In an evaluation using both ground truth samples with known cyst numbers and manually annotated images, the computer vision approach produced accurate nematode cyst counts, as well as accurate cyst segmentations. Based on such segmentations, morphological cyst features could be computed that served to reveal phenotypical differences between nematode populations in different soils and in populations observed before and after the sugar beet planting period. Going beyond fast and precise cyst counting, the image-based approach enables quantification of cyst density and phenotyping of morphological cyst features under different conditions, providing the basis for high-throughput applications in agriculture and plant breeding research. Source code and annotated image data sets are freely available for scientific use.