Leaf segmentation in plant phenotyping: a collation study

Hanno Scharr, Massimo Minervini, Andrew P. French, Christian Klukas, David M. Kramer, Xiaoming Liu, Imanol Luengo, Jean-Michel Pape, Gerrit Polder, Danijela Vukadinovic, Xi Yin, Sotirios A. Tsaftaris
2015 Machine Vision and Applications  
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 MM and SAT acknowledge a Marie Curie Action: "Reintegration vised fashion, and the other via optimal template selection and Chamfer matching. Overall, we find that although separating plant from background can be accomplished
more » ... ith 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.
doi:10.1007/s00138-015-0737-3 fatcat:c4dmf4exezgi3kiaxdipw3h7ai