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An open-source microprocessor-based sensor for monitoring grazing animal behaviors.

B. R. dos Reis




An open-source microprocessor-based sensor for monitoring grazing animal behaviors.
B. R. dos Reis*, D. Fuka, Z. Easton, R. R. White. Virginia Tech Blacksburg, VA.

Precision animal agriculture is a rapidly expanding field; however, innovations in this sector are limited by availability of low-cost, low-power sensors that have capacity to send data over long distances without reliance on cellular, bluetooth, or internet-based networking. The objective of this study was to construct an open-source, microprocessor-based sensor designed to detect and report location and activity of pastured ruminants. The sensor comprises an Arduino Nano microprocessor ($4), a generic MPU92/50 motion sensor ($8) which contains a 3-axis accelerometer, 3 -axis magnetometer, and a 3-axis gyroscope, a generic GPS receiver ($5), and a RFM95W generic LoRa radio ($7). The Arduino can be programmed flexibly using the open source Arduino IDE software to adjust the frequency of sampling, the data packet to send, and what conditions are needed to operate. The LoRa radio transmits to a Dragino LoRa gateway ($60) which can also be flexibly programmed through the Arduino IDE software to send data to local storage or, in cases where a web or cellular connection is available, to cloud storage. The sensor was powered using a generic 3.7 V, 2000 mAh Lithium ion battery. The battery, unassisted, was able to power the sensor at a 1 Hz sampling rate for approximately 12 h. For solar assistance, the battery can be connected to a 5 V solar panel ($30) using an Adafruit USB/DC/Solar Lithium Ion/polymer charger ($18). We demonstrated the utility of this sensor suite to timestamp animal location data with motion sensor information by deploying the sensor suite on grazing animals over a 24 h period. For this demonstration, sampling once per 3 min was used. Data recovery rates were excellent (>90%) and all sensors were able to maintain power for the duration of the utility demonstration. Future work should refine the power usage and design of sensors to enable verification of GPS data, training algorithms to interpret motion data, and expansion of the sensor suite.

Keywords: accelerometer, GPS, behavior detection.

Biography: Barbara Roqueto dos Reis currently works at Department of Animal & Poultry Sciences, Virginia Polytechnic Institute and State University. Barbara does research in ruminant nutrition and production.