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Published June 7, 2024 | Version 1
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Data From: BioSense: An Automated Sensing Node for Organismal and Environmental Biology

Authors/Creators

  • 1. ROR icon The University of Texas Rio Grande Valley
  • 2. ROR icon The University of Texas at Austin
  • 3. Carnegie Mellon University

Description

Article title

BioSense: An Automated Sensing Node for Organismal and Environmental Biology

 

Authors

Andrea Continaa,b, Eric Abelsonb, Brendan Allisonb, Brian Stokesb, Kenedy F. Sanchezc, Henry M.Hernandezd, Anna M. Keppleb, Quynhmai Tranb, Isabella Kazend, Katherine Brownb, Je'aime H. Powelle, Timothy H. Keittb

 

Affiliations

a Permanent address: School of Integrative Biological and Chemical Sciences, The University of Texas Rio Grande Valley, Brownsville, TX 78520, USA.

b Department of Integrative Biology, The University of Texas at Austin, Austin, TX 78703, USA.

c Carnegie Mellon University, Pittsburgh, PA 15213, USA.

d Department of Physics, The University of Texas at Austin, Austin, TX 78703, USA.

e Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX 78703, USA.

 

Corresponding author’s email address 

Timothy Keitt, tkeitt@utexas.edu. 

 

Abstract

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.

Technical info

Figure 1. The main components of a BioSense node. The basic hardware configuration of each unit includes an integrated a Raspberry-Pi computer board, a BME-280 weather station for air temperature, humidity and pressure data collection protected by a radiation shield, a Polyvinyl Chloride (PVC) waterproof box fitted caps clamped down with stainless steel bolts and nuts, a stereo microphone, two soil moisture probes, and an optional solar-charged battery pack if no electricity plugs are available. A) protective enclosure, B) Raspberry-Pi 4, C) radiation shield, D) Wi-Fi antenna, E) two soil moisture sensors, F) microphone, G) BME280.

Technical info

Figure 2. Real Time Clock (RTC) port and color table.

Technical info

Figure 3. Raspberry Pi cable and power supply configuration before insertion into the waterproof case.

Technical info

Figure 4. Examples of BioSense prototypes deployed in the field at the Brackenridge Field Laboratory, TX (panel A), and at the Balcones Canyonlands National Wildlife Refuge, TX (panel B). Note that the radiation shield in panel A can be modified to accommodate more than three protective discs, if needed. Additionally, the prototype in panel B is connected to a small solar panel.

Technical info

Figure 5. Blue Jay vocalization spectrogram extracted from the BioSense recording with the highest confidence level (0.3118) as provided by BirdNET analysis (panel A). Blue Jay vocalization spectrogram obtained with a separate recording unit while visually confirming the species sighting nearby the microphone for comparison purposes (panel B). The spectrogram of the calls inside the white rectangles in panel A are nearly identical to the Blue Jay signature calls plotted inside the white rectangles in panel B. This result represents a simple validation of the BirdNET analysis implemented through a Raspberry Pi platform which we embedded into the BioSense node configuration. While a thorough investigation of avian species occurrence at the test site is beyond the scope of this manuscript, this illustration provides evidence that at least for common species such as the Blue Jay, BioSense can perform a neural network classification analysis and detect the correct species.

Technical info

Figure 6. Environmental data profiles for air humidity, soil moisture, and ambient temperature during laboratory testing. The capacitive measurement through the ATSAMD10 chip in one soil moisture probe (SM1) showed a sharp decrease over two consecutive timestamps corresponding to the measurements taken on September 25 at 09:20 pm and 09:30 pm. We do not have a clear explanation for these observations, but it is possible that we may have recorded a couple of faulty capacitive readings and we recommend caution with the interpretation of the results. Overall, air temperature and humidity recorded by the BME280 probe ranged between 24.3 °C and 26.2 °C and 48.8 % and 55 %, respectively. These measurements are consistent to the controlled temperature and humidity laboratory condition in which we tested our BioSense prototypes.

Technical info

Initial Raspberry Pi Configuration

Files

BioSense_Configuration.pdf

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Additional details

Software

Repository URL
https://github.com/keittlab/BioSense.git