Special Issue

Call for Papers


Machine Vision and Applications

Special Issue on

Computer Vision and Image Analysis in Plant Phenotyping



Important Dates 
Call for Papers:         Feb 2015
Submission: May 4 2015, 11:59PM Pacific Time
First round decisions:      
June 20 2015
Revision deadline: July 30 2015, 11:59PM Pacific Time
Final round decisions: Aug 30 2015
Online publication: November 2015
Plant phenotyping is the identification of effects on the phenotype (i.e., the plant appearance and behavior) as a result of genotype differences (i.e., differences in the genetic code) and the environment. Previously, the process of taking phenotypic measurements has been manual, costly, and time consuming.  In recent years, non-invasive, imaging‐based methods have become more common. These images are recorded by a range of capture devices from small embedded camera systems to multi-million Euro smart-greenhouses, at scales ranging from microscopic images of cells, to entire fields captured by UAVs
These images need to be analyzed in a high throughput, robust, and accurate manner. UN-FAO statistics show that according to current population predictions we will need to achieve a 70% increase in food productivity by 2050, simply to maintain current global nutrition levels. Phenomics – large-scale measurement of plant traits – is the bottleneck here, and machine vision is ideally placed to help. However, the occurring problems differ from usual tasks addressed by the computer vision community due to the requirements posed by this application scenario.  
Dealing with these new problems has spawned new specialized workshops such as CVPPP (Computer Vision Problems in Plant Phenotyping) which was held for the first time in conjunction with ECCV 2014, and the stand-alone workshop IAMPS (Image Analysis Methods for the Plant Sciences) now in its fourth year
The overriding goal of this special issue is to focus on 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:
  • problem statements accompanied by 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, reconstruction, detection, and identification methods that address unsolved plant phenotyping scenarios 
  • open source implementation, comparison and discussion of existing methods

Authors are encouraged to submit original work that has not appeared in, nor is in consideration by, other journals. Previously published conference papers can be submitted in extended form (with additional supporting experiments and a more detailed technical description of the method). All papers will be subject to expert peer review.

Further information on the process (as well any special issue related updates) are available at: 



The electronic copy of a complete manuscript (10-15 pages in the Machine Vision and Applications publication format http://www.springer.com/computer/image+processing/journal/138?detailsPage=pltci_2116423) should be submitted through the journal manuscript tracking system at the web site: http://www.editorialmanager.com/mvap/ indicating that the contribution is for the special issue “Computer Vision and Image Analysis in Plant Phenotyping”. 

Guest editors (alphabetical order)
Hannah Dee, Aberystwyth University, UK (hmd1@aber.ac.uk)
Andrew French, University of Nottingham, UK (Andrew.P.French@nottingham.ac.uk)
Hanno Scharr, Forschungszentrum Jülich, Germany (h.scharr@fz-juelich.de)
Sotirios Tsaftaris, IMT Lucca, Italy (s.tsaftaris@imtlucca.it)

Website: http://www.plant-phenotyping.org/CVPPP2014-Special-Issue/

Important Dates


Submission: May 4 2015
First decisions:
June 20 2015
Revision deadline: July 30 2015
Final decisions: Aug 30 2015
Online publication:

November 2015


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Some example images from data set