Leaf segmentation in plant phenotyping: a collation study

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.

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Notes

  1. 1.

    http://www.plant-phenotyping.org/CVPPP2014.

  2. 2.

    http://www.phenotiki.com.

  3. 3.

    http://www.garnics.eu/.

  4. 4.

    http://www.dppn.de/.

  5. 5.

    http://www.plant-phenotyping.org/.

  6. 6.

    http://www.plant-phenotyping-network.eu/.

  7. 7.

    http://www.iplantcollaborative.org/.

  8. 8.

    http://www.plant-phenotyping.org/CVPPP2014-dataset.

  9. 9.

    To measure Dice per leaf, we first find matches between a leaf in ground truth and an algorithm’s result that maximally overlap, and then report the Dice (Eq. 1) of matched leaves; for non-matched leaves a zero is reported.

  10. 10.

    This indicates that additional (possibly tailored) evaluation metrics may be necessary, although our testing with some common in the literature did not yield any improvement.

  11. 11.

    See the new Leaf Counting Challenge of CVPPP 2015 at BMVC (http://www.plant-phenotyping.org/CVPPP2015).

  12. 12.

    https://www.codalab.org/.

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Acknowledgments

We would like to thank participants of the 2014 CVPPP workshop for comments and annotators that have contributed to this work.

Author contributions SAT coordinated this collation study. SAT and HS organized the original LSC challenge. SAT, HS, and MM, wrote the paper and performed analysis. All other authors have contributed methods, text, and results. All authors have approved the manuscript.

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Correspondence to Massimo Minervini or Sotirios A. Tsaftaris.

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MM and SAT acknowledge a Marie Curie Action: “Reintegration Grant” (Grant #256534) of the EU’s Seventh Framework Programme (FP7/2007-2013). HS acknowledges funding from EU-FP7 no. 247947 (GARNICS). HS, JMP, and CK acknowledge the support of the German-Plant-Phenotyping Network, which is funded by the German Federal Ministry of Education and Research (Project Identification Number: 031A053). XY, XL, and DK acknowledge the support of US Department of Energy, Office of Science, Basic Energy Sciences Program (DE-FG02-91ER20021) and the MSU centre for Advanced Algal and Plant Phenotyping.

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Scharr, H., Minervini, M., French, A.P. et al. Leaf segmentation in plant phenotyping: a collation study. Machine Vision and Applications 27, 585–606 (2016). https://doi.org/10.1007/s00138-015-0737-3

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Keywords

  • Plant phenotyping
  • Leaf
  • Multi-instance segmentation
  • Machine learning