Using computer vision on herbarium specimen images to discriminate among closely related horsetails (Equisetum).
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Premise:Equisetum is a distinctive vascular plant genus with 15 extant species worldwide. Species identification is complicated by morphological plasticity and frequent hybridization events, leading to a disproportionately high number of misidentified specimens. These may be correctly identified by applying appropriate computer vision tools. Methods:We hypothesize that aerial stem nodes can provide enough information to distinguish among Equisetum hyemale, E. laevigatum, and E . ×ferrissii, the latter being a hybrid between the other two. An object detector was trained to find nodes on a given image and to distinguish E. hyemale nodes from those of E. laevigatum. A classifier then took statistics from the detection results and classified the given image into one of the three taxa. Both detector and classifier were trained and tested on expert manually annotated images. Results:In our exploratory test set of 30 images, our detector/classifier combination identified all 10 E. laevigatum images correctly, as well as nine out of 10 E. hyemale images, and eight out of 10 E. ×ferrissii images, for a 90% classification accuracy. Discussion:Our results support the notion that computer vision may help with the identification of herbarium specimens once enough manual annotations become available.
Published Version (Please cite this version)10.1002/aps3.11372
Publication InfoPryer, KM; Tomasi, C; Wang, X; Meineke, EK; & Windham, MD (2020). Using computer vision on herbarium specimen images to discriminate among closely related horsetails (Equisetum). Applications in plant sciences, 8(6). pp. e11372. 10.1002/aps3.11372. Retrieved from https://hdl.handle.net/10161/21728.
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Professor of Biology
Iris Einheuser Distinguished Professor
Computer vision, medical imaging, and applied mathematics. Current Projects: Image recognition (funded by NSF), stereo and image motion analysis (funded by SAIC), medical imaging and computer-assisted diagnosis, and object recognition.
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