Using Raspberry Pi microcomputers to remotely monitor birds and collect environmental data

Journal Pre-proof Using raspberry pi microcomputers to remotely monitor birds and collect environmental data

Wesley J. McBride, Jason R. Courter PII:

S1574-9541(19)30327-9

DOI:

https://doi.org/10.1016/j.ecoinf.2019.101016

Reference:

ECOINF 101016

To appear in:

Ecological Informatics

Received date:

27 May 2019

Revised date:

25 September 2019

Accepted date:

25 September 2019

Please cite this article as: W.J. McBride and J.R. Courter, Using raspberry pi microcomputers to remotely monitor birds and collect environmental data, Ecological Informatics(2019), https://doi.org/10.1016/j.ecoinf.2019.101016

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

© 2019 Published by Elsevier.

Journal Pre-proof Using Raspberry Pi Microcomputers to Remotely Monitor Birds and Collect Environmental Data

Wesley J. McBridea and Jason R. Courtera,* [email protected] a

Department of Science and Mathematics, Malone University, 2600 Cleveland Ave. NW,

Canton, OH, USA *

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Corresponding author.

Journal Pre-proof Abstract Technological innovations have expanded our ability to understand ecological systems and conserve biodiversity at a time when many species are at risk from climate change and habitat loss. One technology that is predicted to greatly benefit the field of avian ecology is the microcomputer. Raspberry Pi microcomputers are low-cost alternatives to personal computers and are able to house a variety of environmental sensors. Raspberry Pis have recently been used

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to monitor the behavior of birds in artificial nest cavities. Here, we deploy and assess the ability

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of five different Raspberry Pi-based devices to record observational and environmental data at

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avian feeding stations. From January to March 2018, each device generated digital photographs

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and recorded related temperature and wind speed measurements every 10 seconds, for three hours per day, at five locations in Stark County, OH, USA. Among photographs taken, 96%

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were classified as ‘usable’ and no differences were noted in percent of usable photographs

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among devices (P = 0.50). In addition, no differences were noted in the mean number of feeder visits of two common birds species detected by devices and detected by a field observer (P =

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0.39 and 0.60). Collectively, temperature measurements recorded by devices were similar to

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temperatures reported at the Akron-Canton Airport (P = 0.88), but wind speeds were 1.78 ± 0.12 m/s lower (P < 0.001) at field sites. Our results demonstrate the ability of Raspberry Pis to remotely monitor birds and collect site-based environmental data in a cost-effective manner. We provide direction for making economical decisions when selecting cameras and temperature sensors without compromising data quality. While our study focuses on birds, our results are generalizable and could provide direction for remotely monitoring a variety of vertebrate and invertebrate taxa.

Journal Pre-proof Keywords: avian microclimate, ecological monitoring, Internet of Things, remote data collection,

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remote detection device, sensor network

Journal Pre-proof 1. Introduction Technological innovations have expanded our ability to understand ecological systems and conserve biodiversity at a time when many are at risk from climate change and habitat loss (Pimm et al., 2015, Allan et al., 2018). In many cases, multidisciplinary collaboration is needed between field ecologists and tech specialists to identify research needs and integrate appropriate technology to work toward conservation solutions (Joppa 2015). In the field of avian ecology,

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technical advancements such as radar imaging, global location sensors, digital video recorders,

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and RFID technology have reshaped our understanding of migration, nesting, and foraging

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behaviors (Grémillet et al., 2006, Gauthreaux and Livingston, 2006, Prinz et al., 2016, Alarcon-

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Nieto et al. 2018, Bridge et al. 2019); and with recent improvements in battery efficiency, sensor technology, and techniques to analyze crowdsourced data, much is still being learned (La Sorte

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et al., 2018). Allan et al. (2018) recently reviewed the emerging discipline of ‘technoecology’

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and identified eight technologies that are predicted to greatly enhance our understanding of species and their environments that include: bio-batteries, low power and long-range telemetry,

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the Internet of things, swarm theory, 3D printing, mapping molecular movement, and low-power

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computers. One of these technologies that may particularly benefit the field of avian ecology is the low-power computer.

1.2. Raspberry Pi Microcomputers Low-power microcomputers are low-cost alternatives to personal computers that come out of the box fully functional, but they are also highly customizable. One of the most popular examples on the market today is the Raspberry Pi (Jiang et al., 2016) which utilizes a modifiable and open source operating system. The Pi has shown great promise in environmental monitoring applications (Ferdoush and Li, 2014) and possesses many features that are useful to researchers

Journal Pre-proof such as a built-in wireless chip, four USB ports, and 40 General-purpose input/output (GPIO) pins. Collectively this allows the Pi to process, store, and upload data collected by multiple sensors. 1.3. Microcomputer applications in the field of avian ecology Recently, Prinz et al., (2016) demonstrated that Raspberry Pis could be used to record the

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behavior of Acorn Woodpeckers (Melanerpes formicivorus) in artificial nest cavities. The

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recording and uploading of video allowed the researchers to study woodpecker breeding behavior with minimal disturbance and reduced observer effort. An important next step to

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demonstrate the usefulness of the Raspberry Pi in avian field studies would be to attach and

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configure different sensors to the Pi to remotely record digital photographs or videos, while also

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recording environmental data such as temperature and wind speed.

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Currently, behavioral and climatological data are often recorded separately by field-based observers or trail cameras, in conjunction with environmental monitoring devices such as iButton

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loggers or wired digital thermometers (Carroll et al., 2018, Xu et al., 2018). Synthesizing the

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recording of observational and environmental data using low-cost microcomputers could contribute to our understanding of how avian microclimates impact avian behavior (Wachob, 1996, Latimer and Zuckerberg, 2017) in a cost-effective and unobtrusive manner. This information could ultimately help us better understand the mechanisms that birds use to adapt to climate change. Therefore the objective of our study is to assess the ability of five different Raspberry Pi-based devices to collect avian behavioral data and associated environmental data in a remote fashion, and to provide recommendations for customizing Raspberry Pi-based units for generalized use in ecological field studies.

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2. Materials and methods We purchased five Raspberry Pi 3 model B devices, along with power cables and microSD cards that came preloaded with the Raspbian operating system (https://www.raspbian.org/). We also purchased temperature-recording sensors, cameras, and anemometers along with analog to digital conversion chips needed to connect the sensors to the

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Raspberry Pis. A 9-watt solar panel, a solar voltage regulator, a boost regulator, and a 10,000

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milliamp battery were used to provide power for each unit. Each Raspberry Pi and the associated

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water-sensitive electronics were housed in a water-resistant case (Fig. 1), and an anemometer

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and a solar panel were placed on top of each unit. We tested different combinations of temperature sensors and cameras so that each of the five devices had a unique and increasing

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build price (Table 1). Units were mounted to a 1.2-m post staked into the ground at each site

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(Fig. 2).

Journal Pre-proof Fig. 1. The inside of a device used in this study showing the Raspberry Pi, camera, battery, and wires for sensor attachments housed in a water-resistant MTM Survivor Dry Box. The

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anemometer and solar panel are pictured to the right.

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Fig. 2. A Raspberry Pi-based monitoring device deployed at a field site.

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Fig. 3. A block diagram of the device showing the device components and interconnections.

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Hollow triangles represent power connections. Solid triangles represent data connections.

Journal Pre-proof Table 1 The configurations of five Raspberry Pi-based recording devices used in this study arranged from lowest to highest cost. Device

Raspberry Pia

Solar Panelb

Power Chipsc Camerad

Temp. Sensore

1

$35.00

$78.00

$36.00

$21.99

$4.00

2

$35.00

$78.00

$36.00

$21.99

$4.95

3

$35.00

$78.00

$36.00

$23.99

4

$35.00

$78.00

$36.00

5

$35.00

$78.00

$36.00

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l a

$39.99

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Total Device Cost

$44.00

$218.99

$44.00

$219.94

$9.95

$44.00

$226.94

$11.95

$44.00

$234.90

$49.95

$44.00

$282.94

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e r P $29.95

Anemometerf

Raspberry Pi 3 Model B (Raspberry Pi Foundation, Cambridge, UK). 9-Watt solar panel (Voltaic Systems, Brooklyn, NY). c USB/DC/Solar Lithium Ion/Polymer Charger v2 (Adafruit Inc, New York City, NY0 and High Efficiency 1.2 MHz 2A Step Up Converter (Aerosemi Technology Co., Xi’an, China). d Camera #1 - ArduCam 5 MP Camera Module (ArduCam Inc., China), Camera #2 – Camera Module v2 (Raspberry Pi Foundation, Cambridge, UK), Camera #3 – RB-WAV-90 (Waveshare Electronics, Shenzhen, China), Camera #4 – Pi NoIR Camera v2 (Raspberry Pi Foundation, Cambridge, UK), Camera #5 – Logitech C270 Widescreen HD Webcam (Logitech International, Newark, CA). e Sensor #1 – 10K Precision Epoxy Thermistor (Murata Manufacturing, Nagaokakyo, Japan), Sensor #2 – MCP9808 High Accuracy I2C Temp. Sensor Board (Microchip Technology, Chandler, AZ), Sensor #3 – Waterproof DS18B20 Digital Temperature Sensor (Maxim Integrated, San Jose, CA), Sensor #4 – Platinum RTD Sensor (Adafruit Inc., New York City, NY), Sensor #5 – Mesh Protected Weather-Proof Temperature Sensor- SHT10 (Sensirion Holding, Stäfa, Switzerland). f Anemometer Wind Speed Sensor with Analog Voltage Output (Oingsheng Electronic Technology Co., Handan, China). b

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Journal Pre-proof 2.2. Sensor and storage configuration using Python A program was written in Python 3 (see supplemental data file for annotated Python code) that initialized all of the sensors, and every 10 sec the program captured and stored JPEG photographs in a folder in the Raspberry Pi’s directory. Similarly, associated temperature and wind speed data were written to a text file and each time-stamped entry was recorded on a new line and separated with commas for easy conversion of the text file to a CSV file (Fig. 3). The

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program was scheduled to run daily from 09:00 to 12:00 using Cron, a utility in Linux that

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executes programs at specified times. After the daily recording period at 13:00, all photograph

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and temperature and wind speed data were uploaded via Wi-Fi provided by the study locations to

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an offsite Raspberry Pi with a two terabyte external hard drive that served as a database (Fig. 3).

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The data upload used rsync, a default program on Raspbian that creates a SSH connection for the transfer of files between devices. Rsync includes functionality for uploaded files to be deleted

space.

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2.3. Device deployment

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automatically, preventing the data collection devices from running out of hard drive storage

Devices (n = 5) were deployed at five suburban residences in Stark County, Ohio, USA (40°49’38” N, 81°23’04” W), from 18 January to 14 March 2018. Devices were mounted on posts that were staked in the ground one meter from feeding stations (Fig. 2), and feeding stations were stocked with black-oil sunflower seeds during the duration of the study to attract birds. Each device was randomly assigned a starting location and then moved each week so that each device was at each site at least once. For a 20-min period each week at each location, an observer (WJM) recorded birds present at feeding stations concurrently with devices. Blackcapped chickadees (Poecile atricapillus) and Tufted titmice (Baeolophus bicolor) were the most

Journal Pre-proof common species noted and were therefore used to compare observations made by WJM and our remote recording devices. 2.4. Data analysis WJM categorized all digital photographs recorded by devices as ‘usable’ or ‘unusable’ with device number and location information removed to prevent potential bias during data

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analysis. A usable photograph was any picture where a bird was present in the photo and the

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head and wing regions were not obstructed and there was no motion blur. We converted the number of usable photographs, each taken at a 10-s interval, into ‘percent of usable photographs

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when birds were present’ and then compared values among devices using ANOVA. T-tests were

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used to compare mean feeder visits documented by devices and by WJM, and to compare mean

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temperatures and wind speeds recorded collectively across all devices and temperatures and wind speeds reported at the Canton-Akron Airport (CAK) through Weather Underground

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(https://www.wunderground.com/history/airport/KCAK/2018/1/1/DailyHistory.html). We then

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used ANOVA to compare the differences in temperature measurements between each device and

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temperature measurements made at the CAK Airport to assess the performances of our five different temperature sensors. We excluded data from 11 Feb 18 to 17 Feb 18 from analyses because of inconsistencies in device operation, likely resulting from a power issue associated with an extended period of cloud cover. All analyses were performed using JMP 14.0 (SAS Institute, Cary, North Carolina, USA).

3. Results

Journal Pre-proof During our study period, each device was deployed for 44 days, generating photographs every 10 seconds for 3 hours per day. Among photographs taken, 96% were classified as ‘usable’ and no differences were noted in the percent of usable photographs per day among devices (F4,180 = 0.84, P = 0.50). In addition, no differences were noted in the mean number of chickadee or titmouse feeder visits when comparing observations made by WJM and observations recorded by remote detection devices (F1,58 = 0.75, t = -0.87, P = 0.39, and F1,58 = 0.28, t = -0.53, P = 0.60,

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respectively). Collectively, temperature data recorded by devices did not differ from temperature

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data reported by the CAK Airport (x = -0.063, SE = 0.42, t = -0.15, P = 0.88; Table 2); however

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when comparing devices, our least expensive device (Device #1; Table 1) reported temperature

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data that differed more widely from temperatures reported at the CAK Airport than did data collected by Devices #2-5, which contained more expensive temperature sensors (F4,658 = 3.49, P

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= 0.008; Table 1). Mean wind speeds reported by devices were lower than wind speeds reported

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at the CAK Airport (x = -1.78, SE = 0.12, t = -14.6, P < 0.001; Table 2).

Journal Pre-proof Table 2 A comparison between mean temperature (ºC) and wind speed (m/s) measurements recorded by devices and reported at the AkronCanton Airport (CAK), Ohio, USA.

N

CAK Mean

SE

Device Mean

SE

Temperature

665

2.04

0.30

1.97

0.30

Wind Speed

715

5.33

0.091

3.55

0.082

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SE Difference

t

P

0.063

0.42

-0.15

0.88

1.78

0.12

-14.6

< 0.001

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Difference

Journal Pre-proof 4. Discussion Our results suggest that Raspberry Pi microcomputers can be used to accurately record field data at a relatively low price-point (Table 1). No differences were noted in photograph usability between the devices that we tested (P = 0.50), which suggests that increased camera cost does not increase photograph quality enough to justify using a more expensive camera (Table 1). In addition, there were no differences in the mean number of chickadee and titmouse

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feeder visits observed by WJM and recorded by devices (P = 0.39 and 0.60, respectively), which

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suggests that Raspberry Pi-based recording devices could potentially replace human observers in

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avian field studies.

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4.2. Temperature data recording and avian microclimates

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Temperature data collected by devices did not differ from hourly temperature captured at

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the CAK Airport (Table 2), suggesting that meteorological stations may sufficiently approximate site-based temperature conditions at regional scales. Latimer and Zuckerberg (2017) used remote

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sensors to assess temperature differences in fragmented forests and reported that differences

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existed at a 50-km gradient, whereas 650 km would be needed to detect the same differences using meteorological station data alone. Because all of our study sites were located within a 21.3-km radius of the CAK Airport, the lack of temperature differences that we report between the airport and our study sites is not surprising. A potential limitation in our study is that our sensors were placed approximately one meter away from the feeding station where the birds were actually observed, and although we do not anticipate that a biologically meaningful temperature difference existed between where our sensors were located and where birds were actually foraging, differences could exist in other contexts, such as when measuring the temperature inside a nesting cavity. For example, devices based on iButton technology have been

Journal Pre-proof used to monitor temperatures within nesting cavities (Hartman and Oring, 2006) and have even been placed on birds themselves (Arnold and Oswald, 2018). Depending on the study, a researcher could decide whether spending >$30 per unit on smaller and wireless iButton technology is important to more accurately measure the microclimate to which a bird may be responding as opposed to using a less expensive temperature sensor that we describe in this study (Table 1). Given the customizable nature of Pi-based recording devices, installing an iButton

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sensor reader on a Pi-based recording devices or experimenting with alternate placements of the

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fine-scale differences in microclimates are expected.

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temperature sensors that we describe in this study could also be possibilities when important,

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4.3. Device costs and temperature accuracy thresholds

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The differences that we noted in the variability of temperature data collected by Device #1 and Devices #2-5 suggest that there may be a cost threshold for accurate temperature data

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collection (Table 1). Device #1 had the cheapest temperature sensor that cost only $4 (however

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this sensor came bundled with an epoxy coating and the actual uncoated sensor only cost $0.10),

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while the temperature sensors on the other devices ranged from $5 to 50 (Table 1). Device #2 had a temperature chip that was located within the housing of the device while all the other devices had sensors external to the housings (Table 1), suggesting that storing sensors internally had no effect on temperature readings, at least not during winter months in Ohio with limited sun exposure. 4.4. Recording local wind speed and its importance in avian field studies

Journal Pre-proof Wind speeds reported by the CAK airport differed widely from wind speeds reported by devices (P < 0.001; Table 2), suggesting that wind estimates from regional meteorological stations may poorly approximate site-based conditions to which birds actual respond (Grubb, 1975). Higher wind speeds being reported at the airport may have resulted from a lack of wind barriers, whereas each of our study sites was located in a residential area and protected from wind by houses and trees. Wind speed is an important factor in understanding bird

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thermoregulatory behavior of birds, especially in cold temperatures (Mayer et al., 1979, Burger

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et al., 2017). Wolf and Walsberg (1996) reported that increasing wind speeds from 0.4 to 3.0 m/s

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increased the metabolic rates of Verdins (Auriparus flaviceps) by 14% and that solar heat gains

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declined dramatically as wind speed increased. Here we report a 1.78 ± 0.12 m/s difference in mean wind speed reported at the CAK airport and recorded by our devices (Table 2).

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Differences such as this could dramatically impact the results of field studies that use wind data

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reported from regional meteorological stations (Shock et al., 2016). We did not assess the variation in anemometer quality, by cost, as we did for temperature sensors because fewer

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options were available for purchase, but this could easily be assessed in future studies.

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4.5. Solar power issues and recommendations We encountered an issue with power in our devices that resulted in the loss of usable field data from 11 Feb to 17 Feb 18. The issue arose because the solar panels were not producing enough power to both charge the devices and provide power to the peripheral sensors that, over a period of two weeks, resulted in a situation where the Pis were sufficiently powered but the sensors were not. At the outset of our study, we knew that relying on solar power during the winter months in Ohio was not without risk given the limited number of hours of direct sunlight each day to recharge the devices. We maintain that relying on solar power is beneficial to allow

Journal Pre-proof devices to be placed away from wired power sources, but recommend using at least a 12-W solar panel in studies conducted during the winter months in temperate regions. Building a device with the potential for a backup battery to be installed, if needed, or installing an on/off circuit so that Pis turns off during non-recording periods could be other ways to abate potential power issues.

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5. Conclusions

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Taken together, our results show great promise for using the Raspberry Pi to remotely

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and simultaneously collect behavioral and environmental data in avian field studies. While our study focuses on birds, our results are generalizable and could provide direction for monitoring a

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variety of vertebrate or invertebrate taxa. We provide direction for making economical decisions

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when selecting cameras and temperature sensors without compromising data quality. Depending

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on the research need, sensors could also be added to economically record audio and video, to measure the duration of feeder visits using capacitive touch sensing, or to measure additional

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environmental variables such as atmospheric CO2 and relative humidity.

Acknowledgements

We thank five homeowners for allowing us to use their yards as field sites. We thank Malone University IT personnel A. Klemann, S. Campbell, J. Shaffer, C. Edwards, and L. Muriuki for technical assistance associated with device configuration and deployment and K. Collie and two anonymous reviewers for helpful comments to improve this manuscript. This study was supported by the National Science Foundation [Grant #1541342].

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Highlights

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Raspberry Pis can be used to remotely collect avian field data at a low price-point Device-based measures of feeder visits similar to data collected by field observer Local temps measured by devices similar to regional temps, but wind speeds differed Direction for making optimal decisions regarding camera and sensor cost and quality Results generalizable and applicable to remotely monitoring other taxa

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    