[MUSIC]
The Purdue University College of Agriculture has a long
history of impacting research, education, and cooperative extension
to improve agriculture and the role of agriculture in our society.
One of the ways that we continue to do that is in this
rearm of digital agriculture.
It's the fusing of us physical and
social world to make improved data driven decisions and actions.
So you see a website there at the corner, we encourage you to visit that,
purdue.ag/digag.
I'm Dennis Buckmaster, a professor of Agricultural and Biological Engineering.
And I also serve our dean and our associate deans as a Deans Fellow for
Digital Agriculture to coordinate activities in the college,
at the university and through to external stakeholders regarding data driven,
technology intensive agriculture.
One of the projects that we have is the Wabash Heartland Innovation Network.
The WHIN project is not exclusively a Purdue project.
It is a partnership with WHIN the organization,
the community foundations in a ten county region of West Central Indiana.
Of course, Purdue as well as Ivy Tech Community College and
many partner organizations, farms, manufacturing firms, etc.
The focus of the WHIN project is to increase the role of
advanced technology in agriculture and manufacturing.
In this series of brief presentations, you're going to learn
how the WHIN funds are being used to advance the technology in agriculture.
So whether this particular video covers the capturing, the clustering,
the collecting, or the computing, you don't have to wait to see.
But in each of these cases, we are moving forward, the technology and
agriculture, through digital means.
>> Hi, my name is Somali Chaterji.
I lead ICAN.
That is the Innovatory for Cells and Neural Machines.
I will give you a snapshot of IoT and on-device computation for
digital agriculture.
Okay, so we will talk about light bandwidth workloads.
And we will talk about heavy bandwidth workloads
that are very relevant to digital agriculture.
So for the first one,
we have outlier anomaly detection for measuring sensor health.
And the second one, for the heavier bandit workloads, we have object detection.
The motivation for doing on-device computation, so
on device as opposed to offloading everything to the cloud is,
one, we want to decrease the latency and pressure on network bandwith,
two, we want to decrease costs and three, increase security, privacy, etc.
I will also show you some test deployments of a cyber physical system consisting of
sensors for moisture and for
temperature connected to the edge device, which is a Raspberry Pi for
the specific algorithm that we're using is normally detection.
And then we are also using an object detection algorithm
using an Nvidia Jetson.
And I will show you some sensor deployments that have been done
via the WHIN project, which is the Wabash Heartland Innovation Network project.
So the ingredients for a smart farm,
this is from the The FarmBeats paper from NSDI 2017.
FarmBeats is an IoT platform for data-driven agriculture.
And this was motivated by the large number of data streams from ubiquitous and
oftentimes inexpensive sensors that are deployed in the forum for
things such as soil, moisture, pH, etc., right?
And then you have networking using LoRa and BLE.
And then you also have the cloud platform for long-term storage nowadays.
So this is at a high level, what an IoT platform for
a digital agricultural will look fine.
Now, the first little over them snapshot that I'll give you is anomaly detection.
Anomaly detection is essentially
an algorithm that detects rare events by the fact that these events
differ significantly from the majority of the data.
And they are effective for detecting sensor health and
in other cases, I might also tell you if there was a malicious attack on the sensor
because of which the sensor is malfunctioning.
Examples of these data streams in the digital agricultural setting
from moisture, temperature nitrate, they might also be microbiome soil sensors.
We have a example deployments from a lab here at this link, which is a normally
detection for temperature and humidity sensors that are actually
also deployed in the forum's just to show you how the anomaly detection works.
So like I said, anomaly detection algorithms they detect anomalies.
But we want this detection to happen close to the source of the data, so
this is resource efficient.
But the challenges are you have these multimodal data
streams coming from multiple sensors.
Now our approach is the simpler variant of an algorithm is called ARIMA,
which is Auto-Regressive Integrated Moving Average.
And the most sophisticated variant anomaly detection algorithm would be an LSTM,
which is also computationally more expensive.
We see that the ARIMA model, which is represented as a function of ARIMA PDQ,
where P is the number of lab observations included in the regression model.
And then we have D and Q, D is the differencing and Q is the time window.
So we optimize these hyper parameters for the ARIMA model and we run it and
essentially what we're doing is we're tricking the sensor malfunctioning.
And we're also seeing if we need to update the model because there is something
called model DK or concept grid that you see in the bottom right.
Because the fixed model may need to be updated from time to time.
So this is essentially what the three parameters of the ARIMA model are.
We have optimized the three parameters and then we use ARIMA for time for costing.
We see that the Mean Squared Error is 0.124 for our model.
We test using the first 30% samples and
train rather previews in the first 30% samples and
test using 70% and then we tune the model online during the testing phase.
Our goal is to reduce the data transfer from the sensor node to the server,
and we do this essentially using time-series forecasting.
And we change the error tolerance in order to decrease the data
that is sent from the sensing node to the server.
So in this case we see that error tolerance is zero0, and so
100% of the data is sent from the sensor node to the server.
You have the true value in green here, and blackthe predictive value using ARIMA.
In blue, you have the data that is sent, and there was no data that is not sent.
In this case, there are no red dots here.
So in here, you see the data centers is now 63.82%
when the error tolerance is increased 2.10 so
now some of the data is being sent to the server.
Here, we see that the data set is decreasing further,
because we have increased the error tolerance 2.2 to 0.
So now the data set has decreased from 60 something to 50 something and finally,
here, we see as the error tolerance has increased to 3, the data sent drastically
reduced to 7.44%, which is essentially our goal to have.
Okay, error tolerance so that not a lot of the data has been sent to the server.
Now here what you see is a snapshot of the data sent.
There is a second Y axis that gives you the mean squared error.
And on the X axis, you have the error tolerance.
So we see that as the error tolerance is increasing,
the mean squared error of the model is increasing, but
the data sent from the center to the server is decreasing.
So the second snapshot is a heavier bandage snapshot.
And for this, we have object detection.
Now object detection is done in the digital agricultural setting, for
example, if you have these drone imagery,
so you don't want to send all the data to the cloud.
So only when there is some exceptional event that is detected
then you communicate this to the gateway node.
Because object detection is a resource intensive machine learning task, and
what we use here is a convolutional neural net,
specifically a convolutional neural network that is good in terms
of architecture, meaning it has less number of parameters than a regular CNN.
Convolutional neural network and variant is a faster our CNN.
You can see a preview of more details of the algorithm.
We call it approximate at this link.
But essentially what happens is because you're using an approximate
version of the convolutional neural net, we're exiting early.
So we're not going all the way through the neural net but we do early exit for
approximation.
And the reason why we do this is because bandwidth to the cloud is expensive,
processing of the cloud is expensive so
you want to use edge services that are at the form and a centralized,
you should not often scale up as you have these ambiguity status freeze.
So most of the video nowadays Perry's the involve inferencing over continents, and
this is a very compute intensive task.
And these are generally too slow and edge devices to keep up with the life video
stream rate, which is 30 frames per second or they're very memory heavy.
And that's how you read approximate versions of these solutions.
So this is what our sensor node looks like, the HDC2010 is our sensors.
So this is an example of this embedded board with a temperature and
a humidity sensor.
And then, you also saw the LoRa connecting antenna at the top of the board,
and these are embedded devices that we have used for
the first one is the one that is used for anomaly detection.
It's a Raspberry Pi note, it does not have a GPU and it's much less expensive,
whereas an Nvidia Jetson TX2 was more expensive and has a GPU back end.
And this is what we use for object detection.
Now all for object detection,
we have to approximate the convolutional neural network.
There are three ways of approximation that we use,
we downsample the image size, and then we do early exit from the continent.
And the third thing that we do is we change the number of region puzzles that
the foster our CNN model makes and I encourage you to go to the link to
understand a little more of the foster our cnn structure or architecture.
This is an example of the deployment type for you,
there are 30 new supplied through our video sensor collector and gateway nodes.
And we see, in general, the PDR, that is the package delivery rate, is high.
There are some lossy notes but in general, it's pretty good.
Here, we see the T pack which is an experimental form of video
deployment of a wireless VR less mesh network.
And here we have two kinds of networking.
We have the long range LoRa and the short range BLE communication.
And finally, these are some examples of the sensor nodes.
You have a temperature and humidity sensor node.
And here are how the sensor is connected to a wire to
the embedded board which is outside the side.
Thank you.
>> Thanks, Somali, so much for that overview and
some instructional images and conversation about IoT for ag.
I was curious when you talked about the remap.
The autoregressive integrated moving average.
What was the temperature in those plots that you were showing?
>> So for our plots, we actually have a test demo.
So if you go to the video link that is there on what the slides, actually slide
four, you will see that we kinda wanted to give our audience an idea of how it works.
So in our specific video link, and I also have a snapshot of the slide four,
what we did is we have the temperature inside the room.
So this was a test video.
So we had the sensor, which has both temperature and humidity inside there.
So the temperature is around 72 degree Fahrenheit.
And then for anomaly detection, what we did is we stepped outside, I think it was
in the 50s outside, so we got an anomaly score when we stepped outside.
>> Perfect.
>> And we put the sensor near the heater inside.
So that was like a higher temperature.
So just to give an example that if there is a sensor going all right on the field,
so we actually have these same sensors on the field.
Then the drastic changes in temperatures would make the anomaly score
go up showing that there is something wrong with the sensors being supplied, or
the specific sensor that is giving the anomaly.
>> Okay, I think mostly agricultural people, so to the engineer,
it's maybe the sensor going crazy but
usually to us agriculturalists that would be the sensor is right and
the phenomena has gone weird like that Kyle now has the temperature.
If we're following her location, it's not that the GPS sensor is incorrect,
it's that she's actually outside the fence or that sort of thing.
>> Good point.
So I remember having a track with you all here.
So in the machine learning lingo in the machine learning parlance,
anomaly detection is kinda the technical term used for this.
But if you want to kinda make sure we not giving farmers different kind of
a view by using the width anomaly, maybe in other word would be outlier detection.
But in machine learning problems,
anomaly detection is the slightly more formal word.
And again for something that I was like the cow stepping outside the fence.
So if we were measuring like some kinda GPS coordinates or whatever,
since this is a time series modeling, it was good to learn over time that something
that the cow did is out of whack.
>> Yeah. >> So
it's gonna depend on what is the regular phenomenon, and how is the phenomenon
changing, which is insulting in a higher anomaly score if that makes sense.
>> And is it possible to do in, I guess, it would be autoregressive integrated
moving average length, but it wouldn't be exactly tha to try to match
the data that you're measuring to what was expected not from machine learning but
from a biophysical or logistic model.
So maybe as an example, there's a certain path that we would expect
an autonomous vehicle to take back and forth and back and
forth, turning on the ends and that sorta thing.
And so if something got out of whack with regard to that,
that wouldn't necessarily be machine learning.
It's just that you're not on the path that we prescribed, that sorta thing.
>> Yeah.
So this arena that we do is actually detect anomalies.
But you make a good point.
So we do this typically in scenarios like this,
where you have IoT or digital life or any of these kinds of scenarios.
We do this for two reasons.
So one is if the sensor is malfunctioning and
the second thing that I did not make it explicitly at the beginning is if you're
modeling a physical phenomenon like you said.
And what happens is sometimes the phenomenon for whatever reason, say,
if you have a tractor and there is a new machine or
the tractor is following a new path, you might need to actually update the model
because the physical phenomenon that is underlying this has changed.
And this is something that is called model DK or concept drift, and what'll happen is
when you're doing ARIMA, you will see that you're getting a high anomaly score, and
that is not necessarily because some sensor is malfunctioning, but
actual physical phenomenon has updated.
And this is a commonly used thing pushing in data science in general,
that for streaming data, there is a concept, grift.
And because of this concept, grift detection, there will be an anomaly score.
And this will just tell you, hey, you actually need to update your model.
And then the model will be updated with the new parameters that are learned by
the model.
>> Yeah, yep, thanks.
And then just a question about the video, the use of video in ag.
You mentioned some opportunities with the robotic arm and
horticulture or the analytics on livestock movement, that sorta thing.
Do you have any specific next steps that you plan to work in?
Taking your expertise, some tools toward video analytics in ag,
like what's the thing that you're gonna work on next?
>> Yeah, so one of the things I was thinking along the lines of the snapshots
that I have in our video that I presented, so what might happen is,
so if you have something like a moving tractor that is there on the field,
and as we know, the networking on these agricultural fields, that's very sparse.
So you might want to use some of these opportunistic tractors or
whatever that will help you carry the data load.
So if you are there in a specific spot and if you see that a tractor is arriving, for
example, then your object detection algorithm can tell you that,
hey, you have a data for free.
So let's transfer the data using the Data Ferry.
So I'm kinda thinking along the lines of the areas that I work can vary,
you will have IoT and
you will be facilitating networking using some of these object detection algorithms.
So automatically when you have some data very that is coming close to
the place where the data is, the source of the data,
you can trigger the object detection algorithm.
And that will result in the data being loaded into the Data Ferry and
being carried to wherever the farmer wants to carry the data.
So it's automatic.
>> Yep, sounds great.
Yeah, we've been talking about lots of times instead of
just transferring the corn or the beans or the seed or the fertilizer
at the same time that we're transferring product, we need to be transferring data.
So, yep, that's a good idea.
Well, thanks again for
sharing the information and we look forward to future progress, thanks.
>> Thank you, Dennis.
This was great.