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Robust Plant Segmentation by advanced imaging techniques

Scientific paper and poster presentation at the 11th International Joint Conference on Biomedical Engineering Systems and Technologies, January 19 - 21, 2018 in Funchal, Portugal.

The purpose of BIOSTEC is to bring together researchers and practitioners, working on both theoretical advances and applications of information systems, artificial intelligence, signal processing, electronics and other engineering tools in knowledge areas related to biology and medicine.

TIME SCALE scientific paper and poster presentation


Robust Plant Segmentation from Challenging Background with a Multiband Acquisition and a Supervised Machine Learning Algorithm


T. Jerbi1, A. Velez Ramirez1 and D. Van Der Straeten1

1) Laboratoryof FunctionalPlant Biology, GhentUniversity, Belgium


Remote sensing through imaging forms the basis for non-invasive plant phenotyping and has numerous applications in fundamental plant science as well as in agriculture. Plant segmentation is a challenging task especially when the image background reveals difficulties such as the presence of algae and moss or, more generally when the background contains a large colour variability. In this work, we present a method based on the use of multiband images to construct a machine learning model that separates between the plant and its background containing soil and algae/moss. Our experiment shows that we succeed to separate plant parts from the image background, as desired. The method presents improvements as compared to previous methods proposed in the literature especially with data containing a complex background.


The full-text paper can be accessed from the web pages of SciTePress.


The full poster can be downloaded as PDF (0.8MB): TIME SCALE poster - BIOSTEC 2018.

January 27, 2018. Manipulated illustration: TIME SCALE and Ghent University.