[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
realm of digital agriculture.
It's the fusing of our physical and
social worlds to make improved data-driven decisions and actions.
So you'll see a website up there in the corner,
we encourage you to visit that, purdue.ag/digag.
I am Dennis Buckmaster, a Professor of Agricultural and Biological Engineering.
And I also serve our dean and our associate deans as a Dean's 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, we are 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 will have to wait to see.
But in each of these cases,
we are moving forward the technology in agriculture through digital means.
>> Greetings, my name is Dharmendra Saraswat, an Associate Professor
in Agricultural and Biological Engineering Department at Purdue.
Due to COVID-19 situation, I am filing a spring progress report
on the project title Evaluating, Developing and Disseminating UAV,
Sensors and Machine Intelligence Based Technology for
Site-Specific Weed Management from my home.
Before I proceed, I would like to acknowledge my project partners,
namely Dr. Shreyas Sen from Electrical and Computer Engineering Department and
Dr. Bryan Young from Botany and Plant Pathology Department.
I would also like to thank my graduate research assistant Varun Aggarwal for
his research contributions.
Last but not the least, I would like to thank Wabash Heartland Innovation Network,
in short WHIN, for their funding support to make this project possible.
There are four major project objectives.
Objective number one is about evaluating deep learning algorithms and
selecting the ones with high accuracy in identifying weeds.
Whereas the objective number two is to generate datasets
that could be used for satisfying the objective one.
The objective number three is about identifying computers that
meet certain design criteria, then integrate those with UAVs so
that the UAVs can be used for real-time data identification purposes.
And the last objective is about educational outreach so
that we can disseminate project findings by conducting
workshops in counties represented by WHIN.
A number of deep learning algorithms for image classification and
image segmentation are being tried as part of objective one.
Image classification algorithms will provide us
a general sense of crop health related information from the field image.
Whereas image segmentation will be applied to those parts of the field image
where a higher weed density is expected so that we can pinpoint weed type and
its location in order to implement site-specific weed management measures.
Of course, image segmentation gives us more information about the scene.
It comes at a price, it requires more computational power, and
usually about five to six times slower compared to image classification.
Three deep learning classification algorithms were
tested on publicly available database comprising of 453
images to classify for weed species, namely cocklebur,
foxtail, giant ragweed, and redroot pigweed.
About 60% of the images in the database were used for
training, and almost 20% each were used for validation and
testing of the three classification algorithms.
Despite using a small size of database,
the accuracy values obtained from image classification algorithms
were very promising and ranged anywhere from 85 to 99%.
Another publicly available dataset was used for
evaluating the performance of image segmentation algorithms.
And the image dataset had three different objects,
the sugarcane crop, presence of weeds, and soy.
The overall accuracy of segmenting sugarcane crop, weed,
and soy was approximately 96% after 300 epochs of training.
So we are very pleased with the performance of the algorithms that
we have selected for image classification and image segmentation so far.
Please recall that the objective number two
pertains to generating dataset that can be used for
training image classification and image segmentation algorithms.
To enable field data collection using a UAS Varun,
my GRA, has already obtained a remote pilot certificate.
And this would allow him to collect field data by flying
over experimental fields at various heights, speed and time of the day.
And in addition, an image acquisition platform has also been
developed for acquiring images in a greenhouse setting.
Objective three is about identifying various computing platforms.
The design requirements for the computing platform include that it should be
power efficient, it should have high computational capabilities.
It should be lightweight so that it can be carried over unmanned aircraft system.
It should be flexible to changes during flight operation, and
it should also have the capability of real-time data processing.
So for this purpose, we have identified two different setups,
those setups have been bought, and
we look forward to testing them during summer 2020.
Objective four is about educational outreach.
Since we are at a preliminary stage of data collection,
we look forward to reporting on this objective in weeks and months to come.
Thanks for listening to this presentation.
If you have any questions, please send it to the email given on the slide.
>> Dharmendra, thanks very much for that update on the work that you're doing,
related to the weed identification, classification and control.
I did have a question about that image that you had with the weed classification.
What would have been the maturity of the weeds at that time?
>> I would say that, just by looking at the pictures, they were small size
weeds that we typically see during the early seasons in corn and soybean crops.
>> Okay, great.
You mentioned that part of your work is gonna be to generate data for yourself,
of course, and maybe even releasing for others to do similar work.
>> Yes. >> Do you have a guess,
will that help draw businesses to this region,
west-central Indiana, connecting them to Purdue?
>> Yes, we always hope that that is what is going to happen.
And let me also give you a little bit of brief background as to why built
the setup to really generate database from different perspectives.
In the field of deep learning and convolution neural networks, it is not
uncommon to hear that we can generate synthetic images at different angles.
And then that's how we can augment the existing databases.
However, I did not come across any study where they
compared how that would compare with the real-life weed
images that we captured from different perspectives.
So that's why we have built that setup which will allowed us to capture
the same weed at different growth stages from different angles.
And then probably we will generate a synthetic database using one
leaf from one perspective and see how it compares with the real-life dataset.
And then if time permits, we would like to capture at least the same
weed from couple of angles in the real-real conditions.
And that way, we want to really have a good set of databases that
other researchers may also find useful.
And that would give them an opportunity to really come and look for
the dataset that we will be hosting on Purdue resources.
>> I think you captured it nicely, because this data is complex.
And the more dimensions you have, not like XYZ dimensions, but the more angles and
perspectives that you can provide, I think, yes, it's a more robust dataset.
And my last question is, you mentioned in the computing pipeline
putting your processor on board your UAV, UAS.
So you will be carrying the computer up with the imaging cameras?
Or were you considering the pipeline to bring the data down to the ground?
>> Well, in fact,
we are planning to integrate everything with the UAV or UAS itself.
>> Okay. >> So that's why the power requirement,
and the weight of the unit, and the processing capacity,
all three factors were very, very important.
And we think at this point of time that we have a very,
very good solution available to us.
And right now we are testing it out to see
how far we can run with it.
>> Sounds great, I'm optimistic.
>> Thank you.
>> Yeah, but thanks again for the update.
Really appreciate learning about what you're doing.
>> Thanks a lot, and I appreciate the support from WHIN and
the support that I'm getting from you and the director extension.
So thank you very much for doing that.
>> You're very welcome.