Research Module M.Sc., Master Thesis or Bachelor Thesis: Evaluating and extending a software for model free plant species identification

Status open
Supervisors Stöver, Ben
Müller, Kai
Accepting institution Evolution and Biodiversity of Plants
Institute for Evolution and Biodiversity
WWU Münster
Hüfferstraße 1
48149 Münster
Germany

Abstract

Background

Automated methods for taxon identification and automated extraction of morphological character states are increasingly important in evolutionary biology, ecology and taxonomics, because on the one hand the number of taxonomic experts able to perform identification manually decreases and the amount of data (e.g. available digital specimen scans and images) to be processed increases. Powerful automated methods would allow a higher throughput and greater repeatability in taxon identification.

The Intelligent Vision Systems group at the University of Bonn and our group recently developed a system to identify plant species from scans of herbarium specimens (Grimm et al., 2016). Its initial test dataset of a selection of specimens of fern species on which the software achieved recognition accuracies between 94 % and 100 %.

Objectives

In this thesis additional data sets containing specimens of species from other plant taxa shall be tested with the software to explore its capability. Recognition accuracies for distinguishing between herbarium specimens with different qualities from taxa with different morphological diversity shall be evaluated and algorithmic extensions can be made to improve the software.

Depending in the progress an additional task can be to test, whether the software is able to group specimens by the presence of certain morphological character states (e.g. types of leaf margins) instead of distinguishing between taxa. An important question here is the relationship between the computer vision feature detection algorithms used (e.g. SIFT) and the morphological features used by botanists identifying taxa manually.

Herbarium specimen with SIFT vectors


Example output of our software showing SIFT points in a herbarium specimen.

What we offer
  • Individualized supervision and an advanced training in bioinformatical methods.
  • Co-authorship in a journal publication depending on the progress made.
Requirements
  • Interest in working in bioinformatics/biodiversity informatics and computer vision.
Further informationContact

If you are interested to work on this topic in your bachelor or master thesis or in a master research module, please contact Ben Stöver. (Working on other bioinformatics topics related to our research and software is also possible.)