Published June 24, 2022
| Version v3
Dataset
Open
PNS-Cyst
Authors/Creators
- 1. Institute of Imaging and Computer Vision, RWTH Aachen University, Germany
- 2. Julius K¨uhn Institute (JKI) – Federal Research Center for Cultivated Plants, Elsdorf, Germany
- 3. LemnaTec, Aachen, Germany
Description
Data collected by the PheNeSens (Phenotyping of Nematodes with Sensors) project. The images recorded cysts of sugar beet nematode, together with organic debris from the soil sample, left after the soil processing.
All cysts are manually outlined by experts and saved as indexed-PNG images. We used the annotations to train deep neural networks for automatic cyst segmentation in the PheNeSens project.
For details of the data collection and deep learning model training, refer to our paper:
Chen L, Daub M, Luigs H-G, Jansen M, Strauch M and Merhof D (2022) High-throughput phenotyping of nematode cysts. Front. Plant Sci. 13:965254. doi: 10.3389/fpls.2022.965254 [link]
Files
PNS-Cyst-Part1-demo1.png
Files
(7.2 GB)
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