Home Publications Research Grants Inventions & Patents Awards Additional Research Activities Faculties & Institutions Research Areas

High-throughput phenotyping of nematode cysts

Chen L, Daub M, Luigs HG, Jansen M, Strauch M, Merhof D (2022)

---

Publication Type: Journal article

Publication year: 2022

Journal

Book Volume: 13

Article Number: 965254

DOI: 10.3389/fpls.2022.965254

Abstract

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, 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. The computer vision approach enables not only fast and precise cyst counting, but also phenotyping of 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.

Involved external institutions

How to cite

APA:

Chen, L., Daub, M., Luigs, H.-G., Jansen, M., Strauch, M., & Merhof, D. (2022). High-throughput phenotyping of nematode cysts. Frontiers in Plant Science, 13. https://dx.doi.org/10.3389/fpls.2022.965254

MLA:

Chen, Long, et al. "High-throughput phenotyping of nematode cysts." Frontiers in Plant Science 13 (2022).

BibTeX: Download