Funding

Our research work has been partially supported by the EU's FP7 project PHIDIAS.

PHIDIAS

Collaborators

We acknowledge the collaboration of Fabiana Zollo (IMT Lucca, Italy) in the implementation of the graphical user interface of the Phenotiki anaysis software on BisQue. We also thank the support of Kristian Kvilekval (UCSB, California) and Dmitry Fedorov (UCSB, California) for helping us to integrate our software within BisQue.

Citing

The Phenotiki software and design are in the public domain and can be freely used according to the license. If you use the Phenotiki software or parts of it please acknowledge our work by adopting the following citation scheme.

Whenever referring to Phenotiki in general, please cite the web page http://phenotiki.com and the article:

M. Minervini, M. V. Giuffrida, P. Perata, S. A. Tsaftaris, “Phenotiki: An open software and hardware platform for affordable and easy image-based phenotyping of rosette-shaped plants,” The Plant Journal, 2017. In press.

Additionally, technical aspects of the Phenotiki system have been discussed in specialized computer vision journals and conferences and should be cited depending on use cases described below.

If you used the Phenotiki analysis software for plant segmentation please cite also:

M. Minervini, M. M. Abdelsamea, S. A. Tsaftaris, “Image-based plant phenotyping with incremental learning and active contours,” Ecological Informatics, vol. 23, pp. 35–48, Sep. 2014.

If you used the Phenotiki analysis software for leaf counting please cite also:

M. V. Giuffrida, M. Minervini, S. A. Tsaftaris, “Learning to count leaves in rosette plants,” in Proceedings of the Computer Vision Problems in Plant Phenotyping (CVPPP) Workshop, pp. 1.1–1.13. BMVA Press, Sep. 2015.

If you used the Phenotiki analysis software for leaf annotation or the stand-alone Leaf Annotation Tool please cite also:

M. Minervini, M. V. Giuffrida, S. A. Tsaftaris, “An interactive tool for semi-automated leaf annotation,” in Proceedings of the Computer Vision Problems in Plant Phenotyping (CVPPP) Workshop, pp. 6.1–6.13. BMVA Press, Sep. 2015.

The demo images included in the Phenotiki analysis software are part of the Plant Phenotyping Datasets. For any use of these images other than playing with the Phenotiki software please cite also:

M. Minervini, A. Fischbach, H. Scharr, S. A. Tsaftaris, “Finely-grained annotated datasets for image-based plant phenotyping,” Pattern Recognition Letters, vol. 81, pp. 80–89, Oct. 2016.

References

The complete list of publications relevant to Phenotiki.

M. Minervini, M. V. Giuffrida, P. Perata, S. A. Tsaftaris, “Phenotiki: An open software and hardware platform for affordable and easy image-based phenotyping of rosette-shaped plants,” The Plant Journal, 2017. In press.

M. Minervini, A. Fischbach, H. Scharr, S. A. Tsaftaris, “Finely-grained annotated datasets for image-based plant phenotyping,” Pattern Recognition Letters, vol. 81, pp. 80–89, Oct. 2016.

H. Scharr, M. Minervini, A. P. French, C. Klukas, D. M. Kramer, X. Liu, I. Luengo Muntión, J.-M. Pape, G. Polder, D. Vukadinovic, X. Yin, S. A. Tsaftaris, “Leaf segmentation in plant phenotyping: A collation study,” Machine Vision and Applications, vol. 27, no. 4, pp. 585–606, May 2016.

M. Minervini, H. Scharr, S. A. Tsaftaris, “The significance of image compression in plant phenotyping applications,” Functional Plant Biology, vol. 42, no. 10, pp. 971–988, Sep. 2015.

M. Minervini, M. V. Giuffrida, S. A. Tsaftaris, “An interactive tool for semi-automated leaf annotation,” in Proceedings of the Computer Vision Problems in Plant Phenotyping (CVPPP) Workshop, pp. 6.1–6.13. BMVA Press, Sep. 2015.

M. V. Giuffrida, M. Minervini, S. A. Tsaftaris, “Learning to count leaves in rosette plants,” in Proceedings of the Computer Vision Problems in Plant Phenotyping (CVPPP) Workshop, pp. 1.1–1.13. BMVA Press, Sep. 2015.

M. Minervini, C. Rusu, S. A. Tsaftaris, “Computationally efficient data and application driven color transforms for the compression and enhancement of images and video,” Color Image and Video Enhancement. Springer, 2015, ch. 13, pp. 371–393.

M. Minervini, H. Scharr, S. A. Tsaftaris, “Image analysis: The new bottleneck in plant phenotyping,” IEEE Signal Processing Magazine, vol. 32, no. 4, pp. 126–131, Jul. 2015.

M. Minervini, C. Rusu, S. A. Tsaftaris, “Unsupervised and supervised approaches to color space transformation for image coding,” in 21st International Conference on Image Processing (ICIP), Paris, France, Oct. 2014, pp. 5576–5580.

M. Minervini, M. M. Abdelsamea, S. A. Tsaftaris, “Image-based plant phenotyping with incremental learning and active contours,” Ecological Informatics, vol. 23, pp. 35–48, Sep. 2014, Special Issue on Multimedia in Ecology and Environment.

H. Scharr, M. Minervini, A. Fischbach, S. A. Tsaftaris, “Annotated image datasets of rosette plants,” Forschungszentrum Jülich GmbH, Jülich, Germany, Tech. Rep. FZJ-2014-03837, Jul. 2014.

M. Minervini, C. Rusu, S. A. Tsaftaris, “Learning computationally efficient approximations of complex image segmentation metrics,” in 8th International Symposium on Image and Signal Processing and Analysis (ISPA), Trieste, Italy, Sep. 2013, pp. 60–65.

M. Minervini, S. A. Tsaftaris, “Application-aware image compression for low cost and distributed plant phenotyping,” in 18th International Conference on Digital Signal Processing (DSP), Santorini, Greece, Jul. 2013, pp. 1–6.

S. A. Tsaftaris, C. Noutsos, “Plant phenotyping with low cost digital cameras and image analytics,” ser. Environmental Science and Engineering. Springer Berlin Heidelberg, 2009, ch. 18, pp. 238–251.