Multi-Sensor Feeder: Automated and Easy-To-Use Animal Monitoring Tool for Citizens
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00:00
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Transcript: English(auto-generated)
00:00
Yeah, I'd like to welcome you to our talk multi-sensor feeder automated and easy to use bird monitoring tool for Citizens we conducted this work together with the two colleagues who are already named before and further colleague who could not be present today Which is called Thomas battle check and we come from the Institute for geoinformatics in Minster in
00:22
Germany Right Here I can present you our content But I will mainly talk about our so-called multi-sensor feeder, which I will present you in a few minutes But let's start first with the topic of the biodiversity loss So at this point in time more species are threatened than with extensions than ever before
00:44
This is a statistic on the left hand side by the IUC and the International Union for Conservation of nature and they found out that the number of endangered species doubled from 2007 to 2019 to over 14,000 and this is especially true also for birds
01:02
Yeah, this also evident by the graphic on the right hand side which was created by the nature and biodiversity conservation union in Germany and They found out that the total number of breeding pairs falls by 14 percent between 1998 and 2009 so for example for the starlings as a decrease of 42 percent so numbers about 2.6 million
01:27
Yeah, the reason for this are oftentimes anthropogenic impacts So excessive land use and destruction of nature and there is a really a need to change something But to change something we need to know what can we change or where can we do something?
01:43
And that's what we work on So we have three areas which help us in our research subject first citizen science So there are already several projects which are working in this field for example as a project where? Citizens can convert in their backyards or there's an application where you can track a citizen's invasive alien species
02:07
and other possibilities for example a naturalist where you can validate recorded data by other citizens and there are also some quite interesting open source tools like the sense box and open source weather station, which you can also
02:21
Use and operate as citizen Right, however a continuous data gathering is also time consuming and for citizens and therefore it's quite useful to use in Automation this can be done by several sensors for example by an RFID ship camera or even a microphone to record sound and
02:43
It's also possible to combine different sensors and to use further information Yeah, artificial intelligence can be a game-changer in this subject So artificial intelligence can be used to identify an animal But not only to identify an animal to also identify this species of the identified animal
03:04
Then you can use already existing models. You can train your own model and you can combine different parameters So for example, you can use the appearance of the animal or the motion and environment where the animal is living to raise your probability of the prediction which you do with with AI
03:22
right and this brings me to our Research projects we want to develop or are developing an automated and easy to use birds monitoring tool for citizens Which we called a multi-sensor feeder So the overall goal of this work is to contribute to the evaluation
03:41
of the biodiversity in people's gardens or balconies by monitoring birds and environmental factors What's important in this context? So we want all the data which is collected by the stations in the gardens to be available as open access Second we want to identify the species of the birds of the visiting birds automatically
04:04
And we not only want to collect data about the birds visiting the stations but we want also to collect data about the environment around the station and The station should be reproducible affordable and easy to use for anyone Yes, so that every citizens can build up the station on their own which leads to a high distribution of stations and thus to
04:26
a high amount of data Now I come to our approach so the approach is to test different Configurations in terms of the hardware so different RAM of the microcomputer different cameras different kinds of detection how we detect the movement
04:43
Also in terms of the software we change we tried out different options so for example in terms of the data storage or the processing location and For the casing of the station we also tried different Variants for example we changed the kind of timber, but I will talk about it in a minute here
05:02
We had certain criteria during Testing this different configurations. What was important for us is a high usability functionality and a reasonable price Right and with this I come to our main results the multi sensor feeder So on the left hand side you can see all components needed to build up the station
05:22
And I explained them in a minute a bit more detailed on the right hand side You can see the build-up station in a garden mounted to a wooden stake Right and first I will introduce you the technical components so on you on the left hand side You see the different components and on the right hand side you see the name and the corresponding costs for it
05:43
So first of all we have a microcomputer a Raspberry Pi model for B with two Gigabit of RAM we have an camera Which is the version 2 Raspberry Pi camera with 8 megapixels and we use furthermore
06:00
microphone and a balance which includes a load cell and weight sensor Right and furthermore an environmental sensor, which measures temperature and humidity Yes, the price for all these Technical components is shortly below 100 euro and the prices were taken from the online seller berry base
06:23
Because which so we choose this as a vendor as it offered most hardware needed to a reasonable price and was good service Now we come to the casing components So we use mainly a beach wood multiplex paid with a width of nine millimeters out of which we yeah Created the individual parts or cut it them out
06:42
you further more need a plastic lip for the station a wooden title perch where the bird can lens on a case for the camera a roofing felt for weather resistance and some further stuff like screws to attach the station and to assemble it Yeah and at least the casing components cost about 40 euros so that we come to a final price of about 140 euros as
07:04
production price for the whole station Yeah, now I come to the recognition process But firstly you can see the station from a side view the interior is divided into two different parts first space for the footer in the front so the footer
07:23
So that it is reachable by the bird who's landing on the perch in front of the station and in the background You can see some space for the technical computer components like the Raspberry Pi and Of course the roof is removable So if you want to reach it with two to make some adaptions at the technique or to refill the footer
07:48
Right. So the balance in the front is measuring the weight in a short-term interval and the environment sensor at the bottom So there's a hole so that they are connected to the microcomputer measures temperature and humidity every X minute so the user can influence or set a settings how
08:03
Often the temperature or the environmental sensor should measure something Right. And now if a birds lands on the perch Then a movement is starting what we called movement and the balance detects changes in the way So this is the way how we recognize a movement
08:20
Then the camera starts recording and films the bird and a microphone starts recording the environment So the sound of the environment and if a bird is leaving then the movement is over because the balance detects this is again a change because no weight is measured by the balance so the movement is over and with this the camera stops recording and the
08:44
microphone stops also recording and with this the movement is over and the micro computer starts to send the data as movement package to our server and Then the server is able to detect the birds p-size by the uses of artificial intelligence and stores all the data on our server and
09:04
with it the data can be shared and therefore we use in platform where Researchers as well as citizens can here reach all the data collected by the stations right The collected data is available in real time via our API
09:24
So some usual data, of course like a time frame, but especially what is in the red? Yeah, the red field is the environmental data So the temperature and humidity measured by the station and if we look have a look at the movement data The weight of the visiting bird is stored and the surrounding sound is available as well as an AI based
09:46
p-size recognition So in this case, it is a great hit with a prediction score of 96 percent Yeah But our data or the data collected by the stations not only available via API in real time
10:01
But also via our website here You can see on the left hand side the stations which are currently visited by birds and on the right side You can see really one station on our platform Where you can find the recordings of the last three birds who visited our stations as video as audio Also, the weight is shown and also the detected species by our server
10:24
furthermore, you can see some temporal series about the environment data in terms of the temperature and humidity and Furthermore at the bottom. You can see the birds. Who is it? So a total number of birds who visited our state This is the corresponding station yesterday or today
10:44
Yeah with this I want to come to one important point that all of our stuff we are doing is open data So the research code is available on github and Zenodo and we provide an open documentation for our API We use open source tools. For example for the server use Docker flash or endings
11:03
yeah concerning the model who's recognizing the See birds. We use a naturalist data together with a mobile net We chew model made by Google and for our website for the map. We use leaflet as well as react Right, and if you want to build up the station yourself
11:24
We have an do-it-yourself manual online available on github and furthermore you can of course visit our platform to have a look at the different stations and to get some more information about our project Right now I come to our discussion. So we decided to use as microcomputers a Raspberry Pi model for B with two gigabits of RAM
11:46
This is a relatively common tool with a lot of documentation and easy adaptable to our needs So we there were many Compatible sensors available with enough documentation and also the RAM of two gigabits is quite enough for our purpose
12:02
Concerning the camera. We use the Raspberry Pi camera model 2 is 8 megapixels This is due to usability reasons as it is easily attachable and the price is quite reasonable for Such insufficient quality. For example, if we would use the high-quality cam It is much more expensive and not very usable due to the size
12:25
Now I come to the detection of the birds There are quite some different options first the motion sensor could be used and attached near to the camera But therefore manual settings are required like to set a time delay or the sensitivity and this is only many will be
12:41
Changeable and not by software and of course, it's additional sensor But there's also positive arguments so you can detect movement also in the background So not only movement directly in front of the station, but also in the background. This is also true for the pixel change detection This the good thing here is that it is also already installed because we do not need an additional sensor
13:04
You can simply do it with the camera But it's challenging to define a threshold At which pixel change counts as motion and this takes there's a need for permanent Analysis and this requires a lot of processing power Nevertheless interesting like the motion sensor if you also want to cover motion in the background
13:25
But we finally decided to use the balance So a certain change in weight detection counts as movement So that there is a low number of false recordings because the camera only starts recording Is really a weight measured by the balance and thus there's a lower processing requirement which enables us to use a low-cost
13:44
microcontroller In terms of the choice of the which we first used a dark colored multiplex plate including a film layer as weather protection But time by time we recognize that the station with this black material is really heating up and we decided to use
14:01
beach plate instead Which we nevertheless Recommend to use a glaze to make it even more weather protected in terms of the general size It's important that there's enough space for technical components and foot and that it is not too big and still attachable by Everyone for the roof the angle and length is important. So if you look at you take a detailed look
14:28
At the front. There's a small overhang so I mean There is some space for foot. That's a the The bird can really reach it
14:42
In terms of the footer silo, it's similar You need enough space for footer and a sufficient angle that the footer can really roll out and in terms of the balance position It needs to be far enough away from the camera so that the bird can record Can be recorded we also thought about using a perch or a plate instead of a perch
15:03
But if you use a plate There's a lot of place for unwanted stuff like dirt or feckle and this influences in the whole recognition process and just towards measured weights Yeah, then another thing we thought about was Recording videos or images. So if you record video video says more space for information and more
15:25
It is probably more interesting for the citizens because they can really watch the videos Images instead need less disk space and are easier to send via network Finally, we decided to record videos because if we only got one single image of the bird probably only parts of the bird
15:42
depicted Yeah, we now use in 30 frames per second video whereby we use every 10th frame Which is then finally analyzed by our image recognition model concerning the processing You could do the image recognition on the microcontroller or on the server if you do it on the microcontroller
16:04
More processing power for the microcomputer is needed. So probably then two gigabits of RAM are not enough But then if you do it there You only need to send for example short text information like this piece as predicted by the model and then it's not required to use Really Wi-Fi you could also use a low power network like LoRaWAN instead of Wi-Fi
16:27
Right and in terms of sending the data already explained that we send movement package But in addition, we also send environment packages where we send only the environment information like temperature and humidity We do this
16:41
Set there is also possible to answer research question in depending on the environment Yeah, but it's a bit more processing power needed Right Concerning the privacy Stations are built up in garden of private person and the recordings and position of stations are available on home
17:03
Homepage, so it's really a need to ensure the data privacy currently the camera focuses on the purge The background is blurred quite a bit and recordings are only stored when a white is Recognized and the citizens of course need to agree that the data is stored But there are some steps we want to do in the future
17:21
For example, the location could be blurred via hexagons since our dot community is doing this So you then not have these? Yeah, the corresponding locations, but they are kind of blurred in a large scale hexagon Another idea is a lightweighted image recognition to detect unwanted content So that you already got an image recognition on the Raspberry Pi which is
17:45
Identifying unwanted content and if there is a person for example on it and the data is not sent to our server Right. There are also some limitations You need certain do-it-yourself tools For example is 3d printer or further tools like a drill or a saw but this should be available
18:02
Normally in a well-equipped at home workshop. You need to think about the station proportions So a big bird probably is not able to land on the small perch and a small bird probably is Too far away from the footer to reach it if it if he stands on the perch so the more of course the footer is kind of limiting different footer affects the station differently as
18:23
Visiting birds change with the chosen footer. You need to think about weather protection So there's a need to ensure that all the different sensors are weather protected We started to put our stations in nature in May. So until today they are running Let's see how long they will do it And of course, there's a validation need because we are certain science project projects though. This is the
18:45
Only due to the citizen science approach our project is running because this way we can really collect a lot of data But yeah as every citizen is a bit different. The stations are bit different and also the collected data is different and therefore need to be Yeah validated. Yeah. Now I come to future work
19:03
Our idea is to use some further sensors for example Particulate matter loudness sensor or an infrared camera to make also recordings during the night We thought about using an alternative microcontroller, which could lower the production costs We also thought about in standalone mode so that we for example change the network connection to LoRaWAN or cellular or
19:25
That we use not a stable power cable, but instead a battery or solar power We also thought about detection of individual birds so that you do not say there were hundred birds today But 50 times the same bird we thought about the validation this could be also done a bit more automized and
19:43
we thought about training our own model because we now really collect a lot of image data and thus we can Train our own model or combine it with a bit retrain model at sea and of course It's also interesting to make the station available for further organisms So already know there were some squirrels and it would be of course also interesting to track them
20:03
Yeah, and with this I come to the end So our presentation is also available via this link the papers already published its website You can see some more information about our project in general, but of course you can see there the stations and Yeah, why you think about some great questions? You can see a video about the birds who already visited our stations
20:25
Thank you