Call for Papers


ECCV 2014 Workshop on

Computer Vision Problems in Plant Phenotyping (CVPPP 2014)



September 12, 2014 

Swiss Federal Institute of Technology (ETH) Zurich


In conjunction with ECCV 2014 (




Important Dates [Dates are tentative; depend on ECCV camera ready deadline]


Call for Papers:         March 2014
Submission Deadline: June 15 2014, 11:59PM Pacific Time
Notification of Acceptance:      
July 15 2014
Camera Ready Version: July 25 2014, 11:59PM Pacific Time
Workshop Day: September 12 2014



Background: Plant Phenotyping


The goal of this workshop is to showcase computer vision challenges in plant phenotyping. Plant phenotyping is the identification of effects on the phenotype (ie., the plant appearance and behavior) as a result of genotype differences (ie., differences in the genetic code) and the environmental conditions a plant has been exposed to. Knowing the plant phenotypes is a key ingredient for knowledge-based bioeconomy and this, not only literally helps in the efforts to feed the world, but is also essential for feed, fibre and fuel production.


While previously, collection of phenotypic traits was manual, currently noninvasive, imaging-based methods are increasingly utilized in plant phenotyping and resulting images need to be analyzed in a high throughput, robust, accurate, and reliable manner. The occurring problems differ from usual tasks addressed by the computer vision community due to the requirements posed by this application scenario.


Plants are complex, self-changing systems with complexity increasing over time. Typical problems in measuring their actual properties comprise measuring size, shape, 3d surface structure, architecture, and other structural traits of plants and their organs (leaves, fruits, roots etc.) or plant populations, where core problems are e.g. reliable detection and multi-label segmentation of many similar objects, or reconstruction of specular, almost featureless, discontinuous surfaces. When interested in changes over time, for example growth rates, tracking, optical flow or scene flow estimation are required. Inherently the tracked objects change their appearance over time. In some cases images may be acquired under controlled conditions, but typically are taken in challenging environments occurring in the field, in green houses, employing automated and/or affordable acquisition setups.

Unfortunately, without automated and accurate computer vision to extract the phenotypes, a bottleneck is encountered, hampering our understanding of plant biology.

Scope of the Workshop


The overriding goal is to identify key but unsolved problems, expose the current state-of-the-art, and broaden the field and the community. Since plant phenotyping is an important aspect of agriculture and will support the sustainability of our planet and its inhabitants, having new vision scientists enter this field is more crucial than ever. 
We welcome submissions that propose interesting computer vision solutions, but also submissions that introduce challenging computer vision problems in plant phenotyping accompanied with benchmark datasets and suitable performance evaluation methods.

Specific topics of interest include, but are not limited to, the following:


* image data sets defining plant phenotyping challenges, complete with annotations if appropriate, accompanied with benchmark methods if possible, and suitable evaluation methods

* advances in segmentation, tracking, detection and identification methods that address unsolved plant phenotyping scenarios

* open source implementation, comparison and discussion of existing methods


Associated with the workshop is the "Plant Leaf Segmentation Challenge":

We are looking forward to practical and inspiring leaf segmentation solutions stemming from an automated plant phenotyping application!


Paper Formatting: Details on format and submission are available at:


Further information about the workshop, author instructions, and the challenge are available at:

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Some example images from data set