René Gislum

The Oil Radish Growth Dataset for Semantic Segmentation and Yield Estimation

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Data sharing in research is important in order to reproduce results, develop global models, and benchmark methods. This paper presents a dataset containing image and field data from a field plot experiment with oil radish (Raphanus sativus L. var oleiformis) as catch crop after spring barley. The field data consists of fresh weight, dry weight, Carbon content and Nitrogen content from multiple weekly plant samples collected from the plots. The image data consists of images collected weekly prior to the plant samples. A subset of the images corresponding to the plant sampling areas have been annotated pixelwise. In addition to the image and field data, weather data from the growing period is also included in the dataset. The dataset is accompanied by two challenges: 1) semantic segmentation of crops and 2) oil radish yield estimation. The former challenge focuses on data image, while the latter focuses on the field data. Baseline methods and results are provided for both challenges.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
Number of pages8
Publication yearJun 2019
Article number9025602
ISBN (Electronic)9781728125060
Publication statusPublished - Jun 2019
EventComputer Vision Problems in Plant Phenotyping - Long Beach Convention & Entertainment Center, Long Beach, United States
Duration: 17 Jun 2019 → …
Conference number: 2019


WorkshopComputer Vision Problems in Plant Phenotyping
LocationLong Beach Convention & Entertainment Center
LandUnited States
ByLong Beach
Periode17/06/2019 → …

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