A two-step registration-classification approach to automated segmentation of multimodal images for high-throughput greenhouse plant phenotyping

Plant Methods
Michael HenkeEvgeny Gladilin

Abstract

Automated segmentation of large amount of image data is one of the major bottlenecks in high-throughput plant phenotyping. Dynamic optical appearance of developing plants, inhomogeneous scene illumination, shadows and reflections in plant and background regions complicate automated segmentation of unimodal plant images. To overcome the problem of ambiguous color information in unimodal data, images of different modalities can be combined to a virtual multispectral cube. However, due to motion artefacts caused by the relocation of plants between photochambers the alignment of multimodal images is often compromised by blurring artifacts. Here, we present an approach to automated segmentation of greenhouse plant images which is based on co-registration of fluorescence (FLU) and of visible light (VIS) camera images followed by subsequent separation of plant and marginal background regions using different species- and camera view-tailored classification models. Our experimental results including a direct comparison with manually segmented ground truth data show that images of different plant types acquired at different developmental stages from different camera views can be automatically segmented with the average accuracy of 93 %...Continue Reading

References

Nov 5, 2016·Trends in Plant Science·Sotirios A TsaftarisHanno Scharr
Feb 17, 2018·Plant Methods·M Valerio GiuffridaSotirios A Tsaftaris
Mar 15, 2018·Scientific Reports·Ping Zhang, Lihong Xu
Aug 15, 2018·Trends in Plant Science·Asheesh Kumar SinghArti Singh
Oct 1, 2013·Functional Plant Biology : FPB·Ruben IspiryanAnton R Sch Ffner

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