A Precise and Autonomous System for the Detection of Insect Emergence Patterns
Author(s) -
Meghan M. Bennett,
Joseph P. Rinehart,
George D. Yocum,
Ian Yocum
Publication year - 2019
Publication title -
journal of visualized experiments
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.596
H-Index - 91
ISSN - 1940-087X
DOI - 10.3791/58362
Subject(s) - computer science , measure (data warehouse) , sample (material) , microcontroller , data collection , arduino , sample size determination , insect , real time computing , embedded system , data mining , biology , statistics , ecology , mathematics , chemistry , chromatography
Existing systems to measure insect emergence patterns have limitations; they are only partially automated and are limited in the maximum number of emerging insects they can detect. In order to obtain precise measurement of insect emergence, it is necessary for systems to be semi-automated and able to measure large numbers of emerging insects. We addressed these issues by designing and building a system that is automated and can measure emergence of up to 1200 insects. We modified the existing "falling-ball" system using Arduino microcontrollers to automate data collection and expand the sample size through multiple data channels. Multiple data channels enable the user to not only increase their sample size, but also allows for multiple treatments to be run simultaneously in a single experiment. Furthermore, we created an R script to automatically visualize the data as a bubble plot, while also calculating the median day and time of emergence. The current system was designed using 3D printing so the user can modify the system to be adjusted for different species of insects. The goal of this protocol is to investigate important questions in chronobiology and stress physiology, using this precise and automated system to measure insect emergence patterns.
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