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Automatic Plant Cover Estimation with Convolutional Neural Networks

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dc.contributor.authorKörschens, Matthias
dc.contributor.authorBodesheim, Paul
dc.contributor.authorRömermann, Christine
dc.contributor.authorBucher, Solveig Franziska
dc.contributor.authorMigliavacca, Mirco
dc.contributor.authorUlrich, Josephine
dc.contributor.authorDenzler, Joachim
dc.date.accessioned2021-12-14T10:57:26Z
dc.date.available2021-12-14T10:57:26Z
dc.date.issued2021
dc.description.abstractMonitoring the responses of plants to environmental changes is essential for plant biodiversity research. This, however, is currently still being done manually by botanists in the field. This work is very laborious, and the data obtained is, though following a standardized method to estimate plant coverage, usually subjective and has a coarse temporal resolution. To remedy these caveats, we investigate approaches using convolutional neural networks (CNNs) to automatically extract the relevant data from images, focusing on plant community composition and species coverages of 9 herbaceous plant species. To this end, we investigate several standard CNN architectures and different pretraining methods. We find that we outperform our previous approach at higher image resolutions using a custom CNN with a mean absolute error of 5.16%. In addition to these investigations, we also conduct an error analysis based on the temporal aspect of the plant cover images. This analysis gives insight into where problems for automatic approaches lie, like occlusion and likely misclassifications caused by temporal changes.en
dc.identifier.doi10.18420/informatik2021-039
dc.identifier.isbn978-3-88579-708-1
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/37703
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofINFORMATIK 2021
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-314
dc.subjectComputer Vision
dc.subjectBiodiversity
dc.subjectDeep Learning
dc.subjectPlant Cover
dc.titleAutomatic Plant Cover Estimation with Convolutional Neural Networksen
gi.citation.endPage516
gi.citation.startPage499
gi.conference.date27. September - 1. Oktober 2021
gi.conference.locationBerlin
gi.conference.sessiontitleWorkshop: Computer Science for Biodiversity (CS4BIODiversity)

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P314 - INFORMATIK 2021 - Computer Science & Sustainability