Preprint Article Version 1 This version is not peer-reviewed

A “Smart” Trap Device for Detection of Crawling Insects and Other Arthropods in Urban Environments

Version 1 : Received: 23 June 2018 / Approved: 25 June 2018 / Online: 25 June 2018 (08:35:40 CEST)

How to cite: Eliopoulos, P.; Tatlas, N.; Rigakis, I.; Potamitis, I. A “Smart” Trap Device for Detection of Crawling Insects and Other Arthropods in Urban Environments. Preprints 2018, 2018060375 (doi: 10.20944/preprints201806.0375.v1). Eliopoulos, P.; Tatlas, N.; Rigakis, I.; Potamitis, I. A “Smart” Trap Device for Detection of Crawling Insects and Other Arthropods in Urban Environments. Preprints 2018, 2018060375 (doi: 10.20944/preprints201806.0375.v1).

Abstract

We introduce a device for automatic detection and reporting of crawling insects in urban environments. It is a monitoring device for urban pests that complies with the context of smart homes, smart cities and is compatible with the emerging discipline of the Internet of Things (IoT). We believe it can find its place to every room of a hotel, hospital, military camp and residence. This box-shaped device attracts targeted insect pests, senses the entering insect and takes automatically a picture of the internal space of the box. The picture is communicated through the Wi-Fi commonly found in such establishments to an authorized person/stakeholder receiving the picture to take proper action. The e-trap includes strong attractants (pheromone and/or food) to increase capture efficiency. The insect is trapped on the sticky floor of the device. The device carries the necessary optoelectronic sensors to guard all entrances of the trap. As the insect enters it interrupts the infrared light source. This triggers a detection event; a picture is taken, and a time-stamp is set before reporting the event through the Wi-Fi. The device can be integrated seamlessly in urban environments and operates unobtrusively to human activities. We report results on various insect pests and depending on the insect species, can reach a detection accuracy ranging from 96-99%.

Subject Areas

electron insect traps; smart city; IoT

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