TY  - JOUR
SP  - 585
N2  - 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.
SN  - 0932-8092
PB  - Springer
A1  - Scharr, Hanno
A1  - Minervini, Massimo
A1  - French, Andrew P.
A1  - Klukas, Christian
A1  - Kramer, David M.
A1  - Liu, Xiaoming
A1  - Luengo, Imanol
A1  - Pape, Jean-Michel
A1  - Polder, Gerrit
A1  - Vukadinovic, Danijela
A1  - Yin, Xi
A1  - Tsaftaris, Sotirios A.
IS  - 4
JF  - Machine Vision and Applications
UR  - http://dx.doi.org/10.1007/s00138-015-0737-3
Y1  - 2016///
TI  - Leaf segmentation in plant phenotyping: a collation study
AV  - none
VL  - 27
EP  - 606
ID  - eprints3482
ER  -