CVPPA, held in conjunction with ICCV 2023, aims to advance computer vision for plant and crop applications. Plant phenotyping, which involves studying the effects of genetic and environmental factors on plant structure and function, is crucial for sustainable agriculture. Traditionally, phenotypic traits were manually collected, but now sensor-based methods are preferred for non-invasive phenotyping. However, the analysis of these observations requires automated and accurate computer vision approaches to overcome bottlenecks that hinder our understanding of plant biology and limit our ability to ensure a sustainable food supply for a growing population in an unpredictable environment.
|Submission due (full paper or extended abstract)||21 July 2023|
|Notification of acceptance||11 August 2023|
|Camera-ready (papers and abstracts):||21 August 2023|
|Workshop date:||2 October 2023 (PM)|
Paper Submission on CMT
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.
Computer vision in agriculture problems tend to revolve around automation problems, such as automated detection of plant organs for automated weeding or harvest. The images are acquired in a variety of contexts: forests, fields, greenhouses or controlled environments, packing houses, etc.
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 and agricultural scenarios;
- open source implementations, comparison and discussion of existing methods and annotation tools;
- image data sets defining plant phenotyping or agricultural tasks, complete with annotations if appropriate, accompanied with benchmark methods if possible, and suitable evaluation methods; and
- challenge contributions advancing the state of the art in the:
Further information and submission guidelines:
We welcome both full papers and extended abstracts. Full papers will be included in the ICCV 2023 proceedings while extended abstracts will appear only on the CVPPA website. Full papers as well as abstracts undergo double blind peer-review. Further information about the workshop, author instructions, submission guidelines, and the challenges are available at this website, https://cvppa2023.github.io/ .
We are looking forward to inspiring solutions for automated plant phenotyping and agricultural applications!