PNS-Cyst

Zenodo (CERN European Organization for Nuclear Research)

Abstract

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:

Authors 6

1. Long Chen Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision, RWTH Aachen University, Germany

2. Matthias Daub

   Julius Kühn-Institut

   Affiliation as printed

   Julius K¨uhn Institute (JKI) – Federal Research Center for Cultivated Plants, Elsdorf, Germany

3. Hans-Georg Luigs
   Affiliation as printed

   LemnaTec, Aachen, Germany

4. Marcus Jansen
   Affiliation as printed

   LemnaTec, Aachen, Germany

5. Martin Strauch Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision, RWTH Aachen University, Germany

6. Dorit Merhof Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision, RWTH Aachen University, Germany