An interactive tool for semi-automated leaf annotation

By Massimo Minervini, Mario Valerio Giuffrida, Sotirios A. Tsaftaris. 2015

20 citations recorded by the source corpus.

High throughput plant phenotyping is emerging as a necessary step towards meeting agricultural demands of the future. Central to its success is the development of robust computer vision algorithms that analyze images and extract phenotyping information to be associated with genotypes and environmental conditions for identifying traits suitable for further development. Obtaining leaf level quantitative data is important towards understanding better this interaction. While certain efforts have been made to obtain such information in an automated fashion, further innovations are necessary. In this paper we present an annotation tool that can be used to semi-automatically segment leaves in images of rosette plants. This tool, which is designed to exist in a stand-alone fashion but also in cloud based environments, can be used to annotate data directly for the study of plant and leaf growth or to provide annotated datasets for learning-based approaches to extracting phenotypes from images. It relies on an interactive graph-based segmentation algorithm to propagate expert provided priors (in the form of pixels) to the rest of the image, using the random walk formulation to find a good per-leaf segmentation. To evaluate the tool we use standardized datasets available from the LSC and LCC 2015 challenges, achieving an average leaf segmentation accuracy of almost 97% using scribbles as annotations. The tool and source code are publicly available at http://www.phenotiki.com and as a GitHub repository at https://github.com/phenotiki/LeafAnnotationTool.

Subfields: Computer Vision and Pattern Recognition, Ecology, Plant Science, Human-Computer Interaction

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Xiaoming Liu

Citations in the corpus

Finely-grained annotated datasets for image-based plant phenotyping (2016, methodology): “In fact, most experts now agree that lack of reliable and automated algorithms to analyze these vast datasets forms a new bottleneck in our understanding of plant biology and function (Minervini et al., 2015b).”
segmentation in plant phenotyping: a collation study. Machine Vision and Applications (2017, background): “Currently, such data are not publicly available but we are working to release such curated datasets and dedicated annotation tools to the community [38,37].”
Leaf segmentation in plant phenotyping: a collation study (2015, background): “Currently, such data are not publicly available but we are working to release such curated datasets and dedicated annotation tools to the community [38,37].”