BioSense: An automated sensing node for organismal and environmental biology
Andrea Contina,
Eric Abelson,
Brendan Allison,
Brian Stokes,
Kenedy F. Sanchez,
Henry M. Hernandez,
Anna M. Kepple,
Quynhmai Tran,
Isabella Kazen,
Katherine A. Brown,
Je’aime H. Powell,
Timothy H. Keitt
Affiliations
Andrea Contina
School of Integrative Biological and Chemical Sciences, The University of Texas Rio Grande Valley, Brownsville, TX 78520, USA; Department of Integrative Biology, The University of Texas at Austin, Austin, TX 78703, USA
Eric Abelson
Department of Integrative Biology, The University of Texas at Austin, Austin, TX 78703, USA
Brendan Allison
Department of Integrative Biology, The University of Texas at Austin, Austin, TX 78703, USA
Brian Stokes
Department of Integrative Biology, The University of Texas at Austin, Austin, TX 78703, USA
Kenedy F. Sanchez
Carnegie Mellon University, Pittsburgh, PA 15213, USA
Henry M. Hernandez
Department of Physics, The University of Texas at Austin, Austin, TX 78712, USA
Anna M. Kepple
Department of Integrative Biology, The University of Texas at Austin, Austin, TX 78703, USA
Quynhmai Tran
Department of Integrative Biology, The University of Texas at Austin, Austin, TX 78703, USA
Isabella Kazen
Department of Physics, The University of Texas at Austin, Austin, TX 78712, USA
Katherine A. Brown
The Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Cavendish Laboratory, University of Cambridge, Cambridge CB3 0HE, UK
Je’aime H. Powell
Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX 78758, USA
Timothy H. Keitt
Department of Integrative Biology, The University of Texas at Austin, Austin, TX 78703, USA; Corresponding author.
Automated remote sensing has revolutionized the fields of wildlife ecology and environmental science. Yet, a cost-effective and flexible approach for large scale monitoring has not been fully developed, resulting in a limited collection of high-resolution data. Here, we describe BioSense, a low-cost and fully programmable automated sensing platform for applications in bioacoustics and environmental studies. Our design offers customization and flexibility to address a broad array of research goals and field conditions. Each BioSense is programmed through an integrated Raspberry Pi computer board and designed to collect and analyze avian vocalizations while simultaneously collecting temperature, humidity, and soil moisture data. We illustrate the different steps involved in manufacturing this sensor including hardware and software design and present the results of our laboratory and field testing in southwestern United States.
