# High-throughput Phenotyping of Nematode Cysts

arXiv: https://arxiv.org/abs/2110.07057 · HTML: https://mlsanity.com/paper/2110.07057/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 13 Oct 2021.

## Builds on (most cited first, 4 of 4)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [A CNN Framenwork Based on Line Annotations for Detecting Nematodes in Microscopic Images](https://mlsanity.com/paper/2004.09795/) (2020)

## Built on by (most cited first, 1 of 1)

- [SortedAP: Rethinking evaluation metrics for instance segmentation](https://mlsanity.com/paper/2309.04887/) (2023)

## Abstract (arXiv)

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.