Flying Insect Classification with Inexpensive Sensors

被引:98
作者
Chen, Yanping [1 ]
Why, Adena [2 ]
Batista, Gustavo [3 ]
Mafra-Neto, Agenor [4 ]
Keogh, Eamonn [1 ]
机构
[1] Univ Calif Riverside, Dept Comp Sci & Engn, Riverside, CA 92521 USA
[2] Univ Calif Riverside, Dept Entomol, Riverside, CA 92521 USA
[3] Univ Sao Paulo, BR-09500900 Sao Paulo, Brazil
[4] ISCA Technol, Riverside, CA USA
基金
巴西圣保罗研究基金会;
关键词
Automate insect classification; insect flight sound; insect wingbeat; Bayesian classifier; flight activity circadian rhythm; WING-BEAT; AUTOMATED IDENTIFICATION; CULEX-QUINQUEFASCIATUS; FLIGHT SOUNDS; MOSQUITOS; FREQUENCY; DENSITY; SINGLE;
D O I
10.1007/s10905-014-9454-4
中图分类号
Q96 [昆虫学];
学科分类号
摘要
The ability to use inexpensive, noninvasive sensors to accurately classify flying insects would have significant implications for entomological research, and allow for the development of many useful applications in vector control for both medical and agricultural entomology. Given this, the last sixty years have seen many research efforts on this task. To date, however, none of this research has had a lasting impact. In this work, we explain this lack of progress. We attribute the stagnation on this problem to several factors, including the use of acoustic sensing devices, the overreliance on the single feature of wingbeat frequency, and the attempts to learn complex models with relatively little data. In contrast, we show that pseudo-acoustic optical sensors can produce vastly superior data, that we can exploit additional features, both intrinsic and extrinsic to the insect's flight behavior, and that a Bayesian classification approach allows us to efficiently learn classification models that are very robust to overfitting. We demonstrate our findings with large scale experiments, as measured both by the number of insects and the number of species considered.
引用
收藏
页码:657 / 677
页数:21
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