28.08.2020 – Online

ECCV 2020 Workshop: Computer Vision Problems in Plant Phenotyping (CVPPP 2020)

Due to Covid-19 ECCV and its workshops will be held online.

Please refer to the event website for most up-to-date information


After the successful CVPPP workshops from recent years at ECCV, BMVC, ICCV, and CVPR, CVPPP this year is held in conjunction with ECCV 2020. The goal of this sixth workshop is to continue to showcase the challenges raised by and extend the state of the art in computer vision for plant phenotyping.

Call for Papers

Plant phenotyping is the identification of effects on plant structure and function (the phenotype) resulting from genotypic differences (i.e., differences in the genetic code) and the environmental conditions a plant has been exposed to. Knowledge of plant phenotypes is a key ingredient of the knowledge-based bioeconomy, which not only literally helps to feed the world, but is also essential for feed, fibre and fuel production. We want to identify key but unsolved problems, expose the current state-of-the-art, and broaden the field and the community.

Specific topics of interest include, but are not limited to, the following:
• advances in segmentation, tracking, detection, reconstruction and identification methods that address unsolved plant phenotyping scenarios
• open source implementation, comparison and discussion of existing methods and annotation tools
• image data sets defining plant phenotyping challenges, complete with annotations if appropriate, accompanied with benchmark methods if possible, and suitable evaluation methods. Compare e.g. the Plant Leaf Segmentation Challenge (LSC), which spawned from earlier CVPPPs and is meanwhile hosted at CodaLab as permanent competition.
• Challenge contributions advancing the state of the art in LSC or LCC, or the novel Global Wheat Challenge.

Submission due (full paper or 1 page abstract): 15 Jun 2020 (Mon)
Notification of acceptance: 06 Jul 2020 (Mon)
Camera-ready (papers and abstracts): 31 Jul 2020 (Fri)
Workshop date: 28 Aug 2020 (FRI)

Download Flyer (PDF)


EPPN2020 has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 731013