Leaf segmentation in plant phenotyping: a collation study

Scharr, Hanno and Minervini, Massimo and French, Andrew P. and Klukas, Christian and Kramer, David M. and Liu, Xiaoming and Luengo, Imanol and Pape, Jean-Michel and Polder, Gerrit and Vukadinovic, Danijela and Yin, Xi and Tsaftaris, Sotirios A. (2016) Leaf segmentation in plant phenotyping: a collation study. Machine Vision and Applications, 27 (4). pp. 585-606. ISSN 1432-1769

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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.

Item Type: Article
Additional Information: The final publication is available at Springer via http://dx.doi.org/s00138-015-0737-3
Keywords: Plant phenotyping; Leaf; Multi-instance segmentation; Machine learning
Schools/Departments: University of Nottingham UK Campus > Faculty of Science > School of Biosciences > Division of Plant and Crop Sciences
University of Nottingham UK Campus > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1007/s00138-015-0737-3
Depositing User: French, Andrew
Date Deposited: 20 Jun 2016 08:21
Last Modified: 18 Sep 2016 16:54
URI: http://eprints.nottingham.ac.uk/id/eprint/34197

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