Hierarchical classification of pollinating flying insects under changing environments |
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| Affiliation: | 1. Laboratory of Computational Intelligence, Institute of Mathematics and Computer Science, University of São Paulo, Av. Trabalhador São-carlense 400, 13566-590 São Carlos, SP, Brazil;2. Pontifícia Universidade Católica do Paraná, Rua Imaculada Conceição 1155, 80215-901 Curitiba, PR, Brazil;3. Department of Computer Science, University of Helsinki, P.O. Box 68, 00014 Helsinki, Finland;4. School of Computer Science and Engineering, University of New South Wales, NSW 2052, Sydney, Australia;1. TUBITAK National Metrology Institute (TÜBİTAK UME), Gebze-Kocaeli, Türkiye;2. Department of Computer Engineering, Kocaeli University, Kocaeli, Türkiye;1. D.B. Warnell School of Forestry and Natural Resources, University of Georgia, 180 E Green Street, Athens, GA 30602, USA;2. Plantation Management Research Cooperative, Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA;3. Silviculture and Phytosanitary Protection Division, Bioforest SA, Concepcion, Chile;1. Department of Information Science and Technology, Dalian Maritime University, 116026 Dalian, China;2. Center of Microfluidic and Optoelectronic Sensing, Dalian Maritime University, 116026 Dalian, China |
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| Abstract: | Automatic monitoring of flying insects enables quick and efficient observations and management of ecologically and economically important targets such as pollinators, disease vectors, and agricultural pests. Studies on this topic mainly cover the tasks of detection and identification or classification, the latter often guided by the flight sounds of insects. This paper uses domain knowledge and taxonomy information to classify bee and wasp species based on abiotic variables and wing-beat data that change depending on climatic-environmental conditions. We survey the state-of-the-art in hierarchical classification and evaluate the most popular local and global methods for this task on flight data from nine hymenopteran species. We collected the data in Brazilian fields employing an inexpensive optical sensor. Our results show that it is possible to hierarchically classify groups of specimens per species, species, and groups of species according to their wing-beat data at different temperature and relative humidity levels with at least 91% accuracy. Besides benefiting research aimed at building insect classifiers adaptable to natural variations in the environment, this study is a vital step in a series of efforts to design non-invasive species monitoring techniques. |
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