Staff Publications

Staff Publications

  • external user (warningwarning)
  • Log in as
  • language uk
  • About

    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

Record number 497140
Title Leaf segmentation in plant phenotyping : a collation study
Author(s) 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.
Source Machine Vision Applications 27 (2016)4. - ISSN 0932-8092 - p. 585 - 606.
DOI http://dx.doi.org/10.1007/s00138-015-0737-3
Department(s) WUR GTB Tuinbouw Technologie
Publication type Refereed Article in a scientific journal
Publication year 2016
Keyword(s) Leaf - Machine learning - Multi-instance segmentation - Plant phenotyping
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 ((Formula presented.)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.

Comments
There are no comments yet. You can post the first one!
Post a comment
 
Please log in to use this service. Login as Wageningen University & Research user or guest user in upper right hand corner of this page.