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Sci Rep. 2018 Apr 3;8(1):5449. doi: 10.1038/s41598-018-23825-1.

Identification of Migratory Insects from their Physical Features using a Decision-Tree Support Vector Machine and its Application to Radar Entomology.

Hu C1,2, Kong S1, Wang R3,4, Long T5, Fu X6.

Author information

1
Radar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China.
2
Key Laboratory of Electronic and Information Technology in Satellite Navigation (Beijing Institute of Technology), Ministry of Education, Beijing, 100081, China.
3
Radar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China. bit.wangrui@gmail.com.
4
Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China. bit.wangrui@gmail.com.
5
Radar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China. longteng@bit.edu.cn.
6
State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academe of Agricultural Sciences, Beijing, 100193, China.

Abstract

Migration is a key process in the population dynamics of numerous insect species, including many that are pests or vectors of disease. Identification of insect migrants is critically important to studies of insect migration. Radar is an effective means of monitoring nocturnal insect migrants. However, species identification of migrating insects is often unachievable with current radar technology. Special-purpose entomological radar can measure radar cross-sections (RCSs) from which the insect mass, wingbeat frequency and body length-to-width ratio (a measure of morphological form) can be estimated. These features may be valuable for species identification. This paper explores the identification of insect migrants based on the mass, wingbeat frequency and length-to-width ratio, and body length is also introduced to assess the benefit of adding another variable. A total of 23 species of migratory insects captured by a searchlight trap are used to develop a classification model based on decision-tree support vector machine method. The results reveal that the identification accuracy exceeds 80% for all species if the mass, wingbeat frequency and length-to-width ratio are utilized, and the addition of body length is shown to further increase accuracy. It is also shown that improving the precision of the measurements leads to increased identification accuracy.

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