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Automated species identification

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AutorInnen: 
Buschbacher, K., Ahrens, D., Espeland, M., Steinhage, V.
Erscheinungsjahr: 
2020
Vollständiger Titel: 
Image based species identification of wild bees using convolutional neural networks
ZFMK-Autorinnen / ZFMK-Autoren: 
Dr. Dirk Ahrens, Dr. Marianne Espeland
Org. Einordnung: 
Coleoptera, Lepidoptera
Publiziert in: 
Ecological Informatics
Publikationstyp: 
Zeitschriftenaufsatz
DOI Name: 
https://doi.org/10.1016/j.ecoinf.2019.101017
Bibliographische Angaben: 
Buschbacher, K., Ahrens, D., Espeland, M., Steinhage, V. (2020): Image-based species identification of wild bees using convolutional neural networks - Ecological Informatics 55: 101017
Abstract: 

Monitoring insect populations is vital for estimating the health of ecosystems. Recently, insect population decline has been highlighted both in the scientific world and the media. Investigating such decline requires monitoring which includes adequate sampling and correctly identifying sampled taxa. This task requires extensive manpower and is time consuming and hard, even for experts, if the process is not automated. Here we propose DeepABIS based on the concepts of the successful Automated Bee Identification System (ABIS), which allowed mobile field investigations including species identification of live bees in field. DeepABIS features three important advancements. First, DeepABIS reduces the efforts of training the system significantly by employing automated feature generation using deep convolutional networks (CNN). Second, DeepABIS enables participatory sensing scenarios employing mobile smart phones and a cloud-based platform for data collection and communication. Third, DeepABIS is adaptable and transferable to other taxa beyond Hymenoptera, i.e., butterflies, flies, etc. Current results show identification results with an average top-1 accuracy of 93.95% and a top-5 accuracy of 99.61% applied to data material of the ABIS project. Adapting DeepABIS to a butterfly dataset showing morphologically difficult to separate populations of the same species of butterfly yields identification results with an average top-1 accuracy of 96.72% and a top-5 accuracy of 99.99%.