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Leaf segmentation in plant phenotyping: a collation study

Scharr, Hanno; Minervini, Massimo; French, Andrew P.; Klukas, Christian; Kramer, David M.; Liu, Xiaoming; Luengo, Imanol; Pape, Jean-Michel; Polder, Gerrit; Vukadinovic, Danijela; Yin, Xi; Tsaftaris, Sotirios A.

Authors

Hanno Scharr

Massimo Minervini

Christian Klukas

David M. Kramer

Xiaoming Liu

Imanol Luengo psxil1@nottingham.ac.uk

Jean-Michel Pape

Gerrit Polder

Danijela Vukadinovic

Xi Yin

Sotirios A. Tsaftaris



Abstract

Image-based plant phenotyping is a growing application area of computer vision in agriculture. A key task is the segmentation of all individual leaves in images. Here we focus on the most common rosette model plants, Arabidopsis and young tobacco. Although leaves do share appearance and shape characteristics, the presence of occlusions and variability in leaf shape and pose, as well as imaging conditions, render this problem challenging. The aim of this paper is to compare several leaf segmentation solutions on a unique and first-of-its-kind dataset containing images from typical phenotyping experiments. In particular, we report and discuss methods and findings of a collection of submissions for the first Leaf Segmentation Challenge of the Computer Vision Problems in Plant Phenotyping workshop in 2014. Four methods are presented: three segment leaves by processing the distance transform in an unsupervised fashion, and the other via optimal template selection and Chamfer matching. Overall, we find that although separating plant from background can be accomplished with satisfactory accuracy (>>90 % Dice score), individual leaf segmentation and counting remain challenging when leaves overlap. Additionally, accuracy is lower for younger leaves. We find also that variability in datasets does affect outcomes. Our findings motivate further investigations and development of specialized algorithms for this particular application, and that challenges of this form are ideally suited for advancing the state of the art. Data are publicly available (online at http://​www.​plant-phenotyping.​org/​datasets) to support future challenges beyond segmentation within this application domain.

Citation

Scharr, H., Minervini, M., French, A. P., Klukas, C., Kramer, D. M., Liu, X., …Tsaftaris, S. A. (2016). Leaf segmentation in plant phenotyping: a collation study. Machine Vision and Applications, 27(4), doi:10.1007/s00138-015-0737-3

Journal Article Type Article
Acceptance Date Oct 27, 2015
Online Publication Date Dec 12, 2015
Publication Date May 1, 2016
Deposit Date Jun 20, 2016
Publicly Available Date Jun 20, 2016
Journal Machine Vision and Applications
Print ISSN 0932-8092
Electronic ISSN 1432-1769
Publisher Humana Press
Peer Reviewed Peer Reviewed
Volume 27
Issue 4
DOI https://doi.org/10.1007/s00138-015-0737-3
Keywords Plant phenotyping; Leaf; Multi-instance segmentation; Machine learning
Public URL http://eprints.nottingham.ac.uk/id/eprint/34197
Publisher URL http://link.springer.com/article/10.1007%2Fs00138-015-0737-3
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf
Additional Information The final publication is available at Springer via http://dx.doi.org/s00138-015-0737-3

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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