Announcer: The following presentation is part
of the agronomy and horticulture seminar series
at the University of Nebraska Lincoln.
Vikas: Good afternoon everyone welcome
to the agronomy and horticulture seminar.
My name is Vikas Belamkar
and I'm a research assistant professor here
in the department.
If you're attending the seminar for the first time
we generally have a 40 to 50 minutes seminar
and it will be followed by the Q and A session.
It's my pleasure to introduce our speaker today,
Dr. Asheesh K Singh, who goes by Dr. Danny Singh
from the department of agronomy
at Iowa state University in Ames, Iowa.
Danny is a soybean breeder.
His professional interest is to help improve
agricultural production and use research
and breeding activities for the benefit of farmers
and the agriculture industry.
He is committed to developing superior soybean cultivars
and germplasm for farmers and other stakeholders.
A little bit about his education.
Danny completed his MSE degree in plant breeding
from University of Saskatchewan in 2001
and obtained his PhD in plant breeding in 2007
from the University of Guelph in Canada.
Danny has had an excellent career so far,
he began his professional career working
as a durum wheat breeder
at agriculture and Agri-Food Canada in 2007.
He joined the department of agronomy
at Iowa State University as an assistant professor in 2013
and was promoted to associate professor in 2017
and a full professor in 2020.
He's also the director of graduate education
for plant breeding at Iowa State University.
Over the years he has developed three soybean varieties
more than 40 wheat varieties,
and more than 14 germplasm releases.
And these are being grown in more
than 10 million acres each year.
He has published over 124 peer reviewed article
and these are being cited more than 3000 times.
He's presented over 60 invited talks at national
and international meetings and more
than a 100 media engagements with farmer magazines,
farmer radio station, and extension talks.
He's also won many, many awards and don't have the time
to go through all of them.
But I mentioned one, he was awarded the Monsanto Chair
in soybean breeding, I think it was in 2013
and he's a PI/co-PI on 59 funded grants.
Two things that I personally admire about Danny
are he's a breeder who has practically used
some of these emerging technologies,
especially like the digital phenotyping tools
to improve breeding pipelines
and many in the community would agree
that he's an excellent mentor to students
and also in scientists.
So we are really excited to have Danny present today
at our seminar series.
Danny, welcome to UNL.
Singh: Thank you so much for such kind introduction Vikas
and for the invitation to present today.
It's my pleasure to be here and in today's presentation
I'll go over some of our current work that aim
to expand the building toolbox for ready development.
And that's your request because the aim
of this presentation is to give diverse examples
of interdisciplinary projects that we have going
on in our program here.
And I wanna make sure that I let you know
that I present on behalf of my students post-docs
and staff that get the job done.
And I also acknowledge my Soynomics team
Dr. ‪Baskar Ganapathysubramania, Dr. Arti Singh
and Dr. Soumik Sarkar
with whom I collaborate closely on most
of the interdisciplinary work that I'll present today.
So several years ago at an early stage of our group start
all staff postdocs and students
we came together as a team
and we developed a mission statement and our research goals
I would say it was a very enlightening exercise for us
about self-awareness and self discovery for our young team
at that time, essentially, we see ourselves
as an interdisciplinary group that is focused
on advancing science and empowering farmers through our work
this mission motivated our research goals
that look at the development of new products, discoveries
and insights on traits and outcomes.
And here I've included a word cloud that was made
by soliciting feedback from our team members
during that exercise on things we work
on and are considered important to us.
And as you can naturally see
some of our interests, they motivate us to establish
the interdisciplinary collaborations
which you'll see through the presentation.
So as most of you probably know in the audience you know
that plant breeding involves intricate processes
and combines learnings from several disciplines
to develop varieties that are high yield, desirable
quality characteristics, resistant to biotic
and abiotic stresses and efficient in resource utilization.
Therefore, once we developed our mission statement
and our research goals
the next step we took was based to plan
and prioritize our research projects.
We want to strive to be breeders
and scientists who are aware and in sync with farmer needs.
And as a plant breeding group, our most immediate output.
Our next generation cultivars
that are less resource intensive, have some value added
and meet the present and future needs
of farmers and projected climate challenges.
This requires us to integrate economics and environment
and genetics, which creates this genomic cube
that is an important component for us.
However, having phenomic cube it implies
high throughput phenotyping on ground
and ariel that generates large and complex data.
Therefore we require advanced and nonlinear models
and analyses to extract meaningful and- timely information.
So keeping that in mind
we set up our breeding and research pipeline
which you see here in this bottom right side of the slide
and this is inherently interdisciplinary
and some word unconventional.
We rely a lot on digital tools
and machine learning methods to meet our research goals
including in filter development, it also focused
on the study of above and below ground traits.
One of the work we do we need to justify
how it can directly or indirectly helping
the cultivar development pipeline.
So our students are getting engaged in both sides
of this figured that you see
and we need to be able to increase the response to selection
through better utilization of genetic diversity,
improved accuracy of selection and faster selection time.
We are interested in speeding up of breeding
for resilient crops that can withstand the challenges
of an increasingly variable environment.
And this figure, we noticed that there are several regions
of the US, and these are in the red sheet
and it's are projected to experience almost two third days
in a year with drought
and high temperature stress in the next several decades.
Therefore, we are very interested
in our research to develop tools that provide inclusion
of complex traits, such as drought and heat tolerance
and the suitability to select
for future climate scenarios that are decades away.
Therefore, we continue to explore artificial
intelligence based approaches in our breeding pipeline.
These are examples of cyber enabled breeding
and they give a glimpse of where field breeding
is heading in my opinion.
In the left side video,
you see a breeder with several methods of phenotyping
including a pushcart right here, autonomous group
ground robots, aerial drones, and just collecting data
that was previously inconceivable or difficult,
in the right side video you see a ground robot
collecting images under the canopy.
And yes, these are soybean plants, not an orchard species
but with the combination
of ground and aerial based phenotyping system
we can obtain a lot of useful information
at a scale that was previously considered impossible
and also an Oregon's that were previously
very difficult to phenotype.
So we work relentlessly to keep streamlining
our breeding pipelines.
We look at maximizing returns
and minimizing costs and errors
while we continually update and modernize our tools.
In our breeding pipeline, we have five main stages
and four main decision-making steps.
As you can see the main stages are hybridization,
generation advancement and selection,
progeny row testing, preliminary
and advanced yield testing and commercialization.
In each of these steps, we decided
to include four main decision-making opportunities
together the stages and decision-making choices.
They help guide our research project
identification and methods.
Since we have a large research footprint in area
under experiments and time series data on multiple traits
high-throughput phenotyping, genomics, robotics, automation
and ML they're integral components of the work we do.
In today's presentation,
I'll give several examples on the use
of image based phenotyping and machine learning methods
which are helping improve
the plant breeding pipeline for us.
These will include tools for ground robots
and drone based phenotyping, the various sensors
stress phenotyping, and study of fruit traits.
More specifically in produce presentation
I'll give examples of four main areas of our work.
These are yield related projects,
tools or methods development, stress related,
and then root leaded projects.
The output from these projects
they directly or indirectly relate
to one of the four decision-making opportunities
which are choice of parent, choice of selection method,
choice of data analysis and choice of placement
are in today's presentational
primarily present projects that relate
to the first three types of choices.
Before we get into the four main types of projects
and examples of ongoing things, these are examples
of projects from our team in the last couple of years
you probably are maybe not able to see
because of the font touch showing up on your screen.
But red outline is for projects and tool method
development related to automated drone based phenotyping.
The green outline boxes are for projects that focus
on stress related topics, the purple outline
are projects that lead to yield and root later topics.
And if anyone has an interest in exploring some
of our prior published work in the last couple of years
on ground robots, stress phenotyping,
and yield and root related work,
please access them through my Google scholar page.
Today, I'll explain, as I said earlier
I'll present our unpublished work.
Although several of the projects I'll present today
they are accessible as an archive paper.
And again, can be found on my Google scholar page.
And if you're interested in reading more about
phenomic assisted breeding, especially students
if you have an interest, please check our chapter number 28
which has been phenomics and machine learning
in crop improvement
in my recently published co-authored book
plant bleeding and cultivar development.
In the first of the four main topics
I'll present two projects that relate to yield.
This first example is our work on soybean pod counting.
It is led by Louis Rivera is a PhD student
of Dr Sarkar and Matt Carroll.
Who's a PhD student in my group.
We were motivated by our aim to reduce phenotyping efforts
with harvesting each plot.
Ultimately combine yield is the best data
for a row crop breeder.
However, we also know that yield components
those traits are effective predictors of CDL
particularly pod counts
they correlate very well with CDL.
So our thought was, if we can estimate pod count in plots
we can reduce the need to harvest plots
which is an expensive and time-consuming task.
So I'm not saying that we will not harvest any plot.
I'm just simply saying that
if we can accurately estimate yield
can we forgo harvesting at all locations?
As a first step, we started working on ground robots
for phenotyping through an NSF USDA funding on robotics.
We wanted to explore the ground robots, their utility
along with ML methods to estimate pods in standing plots.
In this video, you can see the robot traveling
through the rows, getting videos of diverse plants
use several 100 different accessions
from the USDA germplasm bank to build our model
ML model to estimate pod counts.
We develop detailed protocols
for the use of ground-based robots, that record videos
while traversing these breeding plots.
To deploy yield estimation framework
on board of this robotic platform, we need to detect
and keep track of individual plots in real time.
Therefore in our two-step framework,
which is built on RetinaNet architecture
with a VGD 19 backbone
it consisted of a feature extraction module followed
by a regression module.
So we built a plot detection tracking framework
that can provide the necessary video frames
for all plants in a specific plot that feeds
into the yield estimation module.
We then developed a deep learning based multi view image
fusion framework for pod detection
and localization that can identify pod
from multiple things
and then created a formula to predict not just the pod
that the robot could see, but also account
for pod that were hidden due to occlusion.
So that's very exciting from a breeding perspective
and initial results are promising
and they can generate reasonable ranks for selection
and this paper is currently under review
but it's also available as an archive paper.
It is also important to mention
that these ground robots can collect data
even in some work, but field conditions
their combines can go to the field.
And our future work will focus on increasing data set sizes
as well as improving the automation
of plot detection, data collection, as well
as integration with aerial based genotyping systems.
In the second project on yieldly work,
it highlight the effort of Tryambak
who is a PhD student with Dr. Sarkar
and John who's a PhD student with me
and now works at Cordova.
We recently presented this work at a workshop and it was one
of the first works on attention based model
for spatial temporal interpretability in yield prediction
can never get that word.
Interpretability in yield prediction.
We used an LSTM model with dual attention mechanism
to accurately predict annual crop yield
using weather variables.
It can learn the most significant weather variables
across 30 weeks in the growing season.
After the spatial attention phase
and according with the LSTM layers that temporal attention
learns the most significant weeks for that yield prediction.
The most important feature of this approach
is that it has both temporal attention, which highlights
the most significant weeks along the growing season
and also a spacial attention, which highlights
which weather variables are most significant in each week.
So what I'm saying is that this method allows breeders
to not only highlight the most important variables
but also figuring out the time step IEV
to break the annual crop yield.
This is useful to us breeders because not only
do we learn what is or are the most
important predictor variables,
but also when it is most important.
The interpretations provided by our model
can help in understanding the impact of weather variability
on agricultural production, in the presence
of climate change,
and also to formulate breeding strategies
to circumvent these climate challenges
For this work we included more
than a 100,0000 unique data points
and about 6,000 unique varieties across 13 years
in the US using the uniform trials.
If we look at the top right graph,
the temporal attention weights highlight
the increasing importance of the features in the time period
of week eight to 10 right here, which falls in about June.
If you look at the graph at the bottom right
you'll notice that average precipitation right here
that is the most important variable for most of the weeks
in the growing season in irradiance is also highlighted
as important by the dual attention model for few weeks.
So we're now working to look
at additional data and additional traits.
Also the next iteration needs
to include molecular information in prediction
as well as some of the biophysical models.
One of the most I would say useful feature of this method
is that it can generate insights
by understanding predictions from the perspective
of what invade.
The insights obtained
by using the model can help to understand
the impacts of weather on yield prediction
and strategize breeding activity that I mentioned earlier
and have a more data-driven decision-making
particularly what invent that plant plasticity is required.
But I would have to say this
that much more work needs to be done
that builds on this project.
Our present two projects that focus on tool
and method development.
In the first example of the method development
and testing project is led by Koushik
who's a PhD student with Dr. ‪Baskar
and Zaki Jubery who was a postdoc fellow
with me and Baskar.
So machine learning models are methods.
They achieve really good performance.
And in image based phenotyping, a lot
of you folks at UNL are already doing this work.
And especially if you have large amount of labeled data
and there is a review article that we put out
in 2018 and in trends and plant science on this topic
but how we're collecting large set of labeled data
or data sets has been a big challenge
for the plant phenotyping community.
These data labeling can be costly, laborious.
It can be time consuming and it reads domain expertise
especially if it is a more complex task
with more complex phenotypes
that requires additional resources
and crowdsourcing has been proposed in use
but it also has its own challenges.
Therefore, we were motivated to ask the question,
can we reduce the amount of labeling needed
for these deep learning models to achieve good performance
in a image based classification task?
And this is where active learning is very useful
because the goal of active learning
is to achieve maximum predictive performance
with a fixed labeling budget.
And it makes it desirable for plant science applications
which are related to the classification tasks
such as these plants, stress ratings
these active learning methods,
they select samples for labeling from a pool
of unlabeled samples to maximize the predictive performance
of the deep learning model.
So in our work we compared four active learning methods,
leased confidence and tropey, deep basion active learning
or DBAL or core set on two very disparate
plant phenotyping problems.
One was the soybean stress identification.
This was a paper that we published
in 2018 in Goshen at all in PNS
and then a weed species classification data set
that was published in 2019 by a group also at all.
And as you can see both these data sets,
they have quite large as you can see the stable,
diverse types as well of classes.
So we use mobile net.
We do architecture for the soybean stress classification
and the resonant 50 architecture
for the weed species classification broadly
because mobile net is a smaller, more compact network
and it's not appropriate for the soybean data set
because we use controlled condition imaging.
Then there's net is a large network
and therefore it is more appropriate
for the V dataset, which was data collected in the field
with imaging and it's more diverse compared
to the controlled environment set for the soybean stress.
And we found that for both soybeans stress and weed dataset
the uncertainty based deep learning methods
that's the least confidence, entropy and DBAL.
They performed better than the random sampling.
This is presentation of that information
and random scent based sample is the core set method
is the other type of method.
And they minimize the amount of labeling that is needed.
And least confidence sampling was the best
performing active learning method for both datasets.
And that considering the different labeling budgets
and DBAL was found to be the best performing
method when labeling budget was small.
So up to 4,000 samples for the soybean
and 2000 samples for the weed dataset
and these results they're helpful
because it helps us to determine the amount of labeling
or labeled data that we need to feed in
to develop our ML models.
And it helps us better manage their resources.
So in the second project Koushik, again
he worked to determine the usefulness
of interpretability methods to explain deep learning
based plant stress, phenotyping.
This is also an archive paper
and this particular work we compared
some of the most popular interpretability methods
including saliency maps, smooth grad,
guided back propagation deep tailor decomposition,
integrated gradients, layer wise relevance propagation,
input time gradients, so just a whole
suit of different interpretability method
to interpret the deep learning model
that is to find pixels
that are important for classification.
And interpretability, I should mention it's becoming very
very important because until recently deep learning
methods were considered a black box
and practitioners were unable to understand
what computer used to generate the outcomes.
For example we can develop a good model that can identify
and quantify a soybean stressed
but all we see are model performance metrics
and no reason as to what the computer learn.
And then we may be less inclined to believe the computer.
So therefore us and other groups
they're very interested in investigating interpretability
for the models that we are developing and using.
So in this project, we trained a dense net, 121 network
for classification of eight different soybeans stresses
both biotic and abiotic stresses
and using a dataset consisting of over 16,000
these RGB images of healthy and stressfully
soy bean leaflets that were captured
in the control condition.
I explained that earlier as well, we find a really
high overall classification accuracy of 1.5.
That's quite good.
And as you can see in the confusion matrix here
on the left side, majority of the class differentiation,
the modern worked quite well.
However, in some instances, for example,
the bacterial plus you'll hear the bacterial blight here.
The model struggled to correctly, a classic it
by the image and even for a human reader
some of these diseases are not easily classified.
So it broadly speaking, I would say that
given a new sample, the program can accurately
identify the stress from an image, but of course
more improvement can be made.
We then used a diverse subset
of the test data to compare these important model features
with those identified by the human expert.
And we observed that most interpretability methods
identify the infected region of the leaflet
as important feature for some
but not all of the correctly classified images.
So you can see some of the examples here.
If you look at potassium deficiency here
most of these models
these are examples of those interpretability models.
You can see that most
of the models they identified similar important features
which is good.
You know that's, there's a very distinct type
of a potassium deficiency symptom that comes.
In other cases.
For example, sudden death syndrome
different models are identified somehow different
or dissimilar important regions.
And this is still perhaps to be expected
as different models are built on somewhere
different base to generate these little importance.
But the unresolved challenge here
is that none of the interpretability methods worked
in all different stress classes.
And therefore at this stage, I would say that we advocate
that these interpretability methods they should be used
as a hypothesis generation that can drive some
of the scientific insight
and more robust models need to be developed.
It is important to clarify
that the model is being used to accurately classify
the outstanding current challenge
is that no universal interpretability method works
in all situation.
So the model works.
What we're talking about is that we don't yet
have a universally acceptable interpretability method
but with further resolution breeders
should be able to use these deep learning based methods
for more accurate phenotyping.
And also check that the model interpretability matches
with the visual with visual reading
heuristic reading as well.
So the third topic of plant stress phenotyping work
I'll give two examples.
It is prior work that has been done in UAV based phenotyping
for crop health and yield prediction.
And it has shown its potential for using a breeding program.
There is it's being done in the V program
the soybean program at UNL
and we showed other programs as well.
For this specific type of stress iron deficiency chlorosis,
there is a really good paper that came out
of Dr. Lorenzo's lab doubles at all where the showed
that are inefficient sclerosis genotyping
can be done successfully using drones.
So our work is aimed to look both
at the ability to predict iron inefficiency chlorosis
but also standardized flight parameters for best practices
and to look into the effect of IDC
on canopy growth and development for a science question.
And it also with an interest
to conduct time series genetic studies.
So we flew at 60 and 30 meters.
So that would be a 200 feet
for 60 and then a 100 feet for the 30 meter.
And we found that accuracy and mean
per class accuracy were not effected
by flight altitude in our experiments.
So those are the different dates than the 30 meter height
and 60 meter height.
And we are looking at both overall accuracy
but also mean per class accuracy
because it's that we are getting good accuracy
in each of the five rating classes.
And this is good news because there's a large increase
in efficiency in flight time,
as well as the area that can be covered
with increases in flight, these flight parameters.
In addition to these, we also looked at canopy growth rates
across time points and farmed some very interesting snips
that when we did the, the Guas
we also grew a complainant steady
on the same farm part without IDC symptom.
And we did not find similar regions
of interest when using this as the control set.
So that seems to give some evidence
that there is diversity in how soybeans recover
from IDC stress and generating time series based insight
on a differential response to ADC and serving radius.
So this project is helping us to set guidelines
for our drone based phenotyping
as well as give some scientifics insights
on the impact of IDC on canopy growth and development.
And we hope to upload an archive paper this year.
In the second project
I'll present work off Sarah Jones on drought tolerance.
Sarah is a PhD student in the group.
And Jay you'll remember if you're in the audience.
Sarah was a student when Jay was here at Iowa state.
So drought tolerance is a complex trait
and you all know this
it's going to become increasingly important.
So our team is working to develop drought tolerant
soybean lines that can withstand these weather extremes
with a lot of effort that went behind this.
We were finally able to set up a good drought
screening field site in Iowa, and we were able
to screen a large unpleasant panel consisting
of work 450 PI lines and checks and replicated trial.
Also breeding populations were planted
in the drought nursery evaluation nursery.
And what we have done is in addition to wilt score
and some of the other leaf, and canopy are a response rates.
We are also interested in some of the other measurements.
For example, researchers have shown the usefulness
of hyperspectral imaging
to accurately phenotype plant stress response
and thermal imaging that measures canopy temperature
has been used to estimate a serving transportation
in the field.
So we've used ground and aerial based thermal imagery
with some success thermal canopy imagery
showing the dark purple
the cooler temperatures compared
to more magenta warmer canopy temperatures.
With ML approach, we are also interested
to be able to estimate wilt score from drone images.
So mounted on these drones sensors
they enabled high-throughput phenotyping
which is necessary for field scale time series experiments.
We are also looking to standardize phenotyping protocols
working with different sensors
and building on past year's experience.
This year we are going to perform
another large germplasm screening
and breeding family screening
for drought tolerance using wilt score, thermal RGB
and hyperspectral cameras radiometer
both through drone and ground data collection.
And this is a visual representation
of all the reflectance data collected across different range
of electromagnetic spectrum, the RGB imagery.
So just on these for the vape bands
the RGB imagery and visual wilt score
from 400 to 750 nanometers is the hyperspectral
from 350 to 2,500.
And then the thermal camera
from 7,500 to 13,500 nanometers.
And these graph show in the figure that wilt scores
across pay bands.
There is a differentiation and that's good news.
One of the goal is to replace ground based imaging
with drone imagery to facilitate more accurate
and more rapid rating for larger drought screening nurseries
and as well as to pick up water spray symptoms
before they're visible to the human eye.
And you can see here, we are taking a hierarchal
approach to improve our phenotyping.
What about our stress
from more simple and then come getting more
and more integration of different sensors
to get as close to accurate phenotyping as possible.
And the next and final project group class, I'll talk about.
So the root system architecture and nodulation
related projects that we are working on
and those a few we've heard me talk about it
I'm very, very passionate about roots.
They are absolutely critical for plants because they help
with water, nutrient uptake, structural strength,
and also in case of a soybean just
with the symbiotic association with bacteria
and it's an often overlooked area of plant science
but our group is quite passionate about this topic.
So currently a large chunk of research effort
in our team, it is dedicated to related research.
And (indistinct) not directly integrated root traits
in they breeding objectives, but more recently
there is a renewed interest in root leader traits.
And this has happened, I would say partly
because of advances in phenotyping capability.
And there's some really cool work that's going on
across different institutions in the US
and overseas as well in developing group imaging softwares.
And we have built an automated route imaging analysis
at Aria in short, this was initially developed
by Baskers group with maize focused on maize root
and then more recently Zaki
and then Kevin for my PhD student who works for Cordova.
We have developed an audio version two, which is focused
on the soybean phenotyping pipeline in early stages.
So this was published
in recently in the planned methods journal
for a mobile low-cost high resolution
root phenotyping system
consisting of imaging platform with computer vision
and MLBs approaches to establish
it's an end to end pipeline with human intervention.
With this high-throughput phenotyping system
has given us the capability and capacity to handle hundreds
of thousands of plants at same time
to integrate time series data capture
we get our work 45 different root traits.
Not all, all of them are important
but they capture different aspects of the root.
And this is an example of the output of a select trait.
We can study multiple accessions and across time points
as I mentioned for numerous states
and you can see here just the time series
for the same genotype and these strings outputs
they can be visualized.
This is just an example
of a visualization for three different genotypes
for different traits in a time series.
So just a lot of information that can be gleaned
from the output of the software.
And again, as I said
there are other really good softwares are paid as well
for root phenotyping.
So building on the audio version two that I showed
in the previous slide, then Kevin, he study
close to 300 diverse lines representing the sample
of genetic diversity in the USDA germplasma collection.
And the main objective there was to study genetic diversity
in group traits for early soybean growth stages.
And as you would expect gynotypes were detected
to be a significant source of variation
but no set pattern was detected
when we explored categories based
on maturity group and growth habits.
We noted that primary root land surface area
of the primary root they were significantly better
in relief than unadapted expression
with large trait diversity was evident
for root traits in the germplasm panel.
And in this paper we also studied the previously
reported roottypes.
And we call them, iroot for informative roots.
These are examples, nutrient foraging as category is based
from previous paper that describes this phenotype
as maximizing the distribution of lateral roots
and top cell at a minimal metabolic cost
to our performed competitors in a nutrient poor soil.
Then we have drought tolerant iroot category
right here which is the falling
the steep deep and shift paradigm long primary root
with steep lateral roots with minimal solidity
then beard type, again, maximizing total root length number
of lateral root branches and traits that give it
that distinct beard shape, just getting really
high maximum group density, the umbrella type
that maximizes root length root width, convex area.
So again a distinct what we call it some relative.
And finally the maximum iroot category,
which is an effort to identify genotypes
that maximize the phenotypic potential
without a particular environment in mind.
So when we study root these little trait correlations
we noted that there was a large set
of traits that provide more general metrics were length
width area strange, good, strong correlations
and forming some single hierarchal clusters
where these relationship held at different studies
standpoints that we looked at.
In the bottom here, the stop here.
So the highest medical values are examples here.
There are some of the highest categories
distinct types are shown.
And this is a graph that shows just how these distinct types
for ranking of the top 10 genotypes
in each category to show
differences between these accessions for different traits.
This here is a cubic clustering heat map
to show the comparisons of genotypes
that phenotype performance, that genogram three
on the left side groups,
the about the 300 different genotypes based
on genetic relatedness using S and P data.
And this first branch
the B indicates the gynotype based clusters
G and F here they're genetically distinct
from the other six and genotype
in these two clusters, they generally showed very
low root trait values indicating their booth performance.
And that's based on this map here, black hair is good.
Red is bad.
And then blue is good.
Orange is worse in terms of ranking.
So second diagram above the heat map
that connects the phenotypic traits based
on these relatedness, the numerical values.
And these are the types that I explained earlier.
So when you look at the red, black heat map here
anything that's black, the harder black would be closer
to one rank, and the most red would be close
to that 300 for the category.
And this genotype group here
see it displayed the high root rate value black
for three of the root categories.
The next is a sub-branch within cluster B
which has strong right here in B, which has strong.
Then you would have been cluster C are also
strong performance cluster A
which is mostly made up of American genotypes.
Wasn't particularly good or bad
but this cluster did have the longest taproot.
So it gives us some insights
of work where different genetic diversity exists
and what is it that is still unused
from a US perspective for breeding.
It also showed the relationship of traits to each other.
That's right here and performance of these 13 root traits
from good to worse rankings and these observations
they match those of the genotype based clusters.
But again you insights were generated
from these subbranches.
So what we are doing now is you're working
on breeding activities to diversify gene pool
for these root traits.
In this project led by PhD student Clayton Carly
it builds on Kevin's work on doing Guas for RSA traits.
So leveraging automated trade extraction
from audio to expand our genetic mapping work
but is an example of the trait diversity within a real
a panel from across a maximum, but minimum the same trait.
And there's a lot of trait variability
which is quite encouraging for us.
And this shows the output from Aria, just to give
the kinds of traits that can be explored coming
from the original image.
This when the final project I'm showing here
is post-root system architecture, study Clayton
and then Zaki
They continued to explore nodulation
and genotypes across times.
There's a lot known about nodulation
but we still don't fully grasp the spatial map
and also the tempo map of nodulation.
So we felt there was a need to acquire quality
consistent and resource efficient data on nodulation.
So one of the skills that has been used
for nodulation is a helpful scale.
That includes visual rating on a scale of zero to five
to evaluate nodules
on taproots compared to the whole root
advances in image based phenotyping.
I think our interest is that we want
to develop more accurate
and more nimble approach for nodule phenotyping.
You can see that these nodules from really small
to really large and just stayed all over the place.
So you can see that their hard to rate
and there's lots of mature
to very young and it's very time consuming
to remove nodules for phenotypic quantification.
So with the snap software that was developed
to get more automated measurement counter for nodule.
We observed an R squared of 0.91 when comparing snap
to the actual ground truth nodules when the same validation,
if we had a higher R squared of 0.99, when we compared snap
with the image based count, and this discrepancy
is likely due to the nodules being knocked
off between imaging and drawing,
as well as the nodule disintegration that may have happened
in the bag, making them nearly impossible
to identify and counting by hand.
We manually remove the nodules
from the samples and about 700 root samples
and took notes of the time record to do so.
And we noticed that that snappy can increase
the rate of phenotyping
to 20 times greater than manual hand quantification.
It allows us to study nodule related traits and soybean.
And when comparing the time, it takes snap
to validate an image versus hand removal of the nodule,
there's a drastic improvement in the reduction of labor.
And it also snap also gives more quantifiable
information that can be collected on each nodule.
This is an archive paper, but it is currently under review.
Hopefully it will be out soon
but we are deploying this snap
for seeing several biological question
and an extensive time study.
So you can just see an example here to show you
that you can acquire traits as a nodule size
and location on the root and overall root
and then the root growth zones.
So this the set comes from several different genotypes.
It's not biased towards one
or two different genotypes, so it's more robust.
At least our intent is, and this also
allows us to get information on the size and you know
where their place of getting that portal as well
as spatial aspect that we were interested in.
So we can also evaluate the different growth
stages that helps us figure out what type
of breeding of what is a breeding target that needs to be
but just some preliminary results I'm showing here
six different accessions for diverse RSA traits.
We put in a study root sampling was done
at different vegetative stage.
And this was done from 12 to 14 applications
per genotype for each of the stage.
This was done over two years, three locations.
And there's a little deviation
in the first growth stage in terms of nodule.
But then you can start seeing
that differences arise so much
of this initial nodulation increase in happening
on the taproot, but some genotypes are also
showing increased inoculation on lateral roots.
And this is greatest variation
and divergence are happening in the stage.
And this is what I meant
the taproot versus the oral group.
So overall total year location mean rank
between genotypes for the total number of nodule
and total number of taproot nodule, certain genotypes.
They seem to be putting more nodules
on the taproot compared to the other genotypes.
And sometimes these trend held
across sleeping across stages too.
And you can see genotype two
in the table here that it adds more nodule
in the lateral node than in it's taproot.
And it's lower in taproot.
Therefore we notice variation
among genotypes and the placement
of the nodule on the main and lateral group.
And prosing some really good research question
that we are exploring now.
And just the port from the image that was taken
and then worked a snap can generate.
It gives us a lot of power.
And finally, through this experiment
we are exploring the relationship between root and shoots
by investigating correlations between about ground traits
growth and development, and then below ground traits.
And this video here showing an example of experiment
we conducted in field that we using to connect these
above and below ground traits.
And this is an example
of a plant where the above part is shown
and then its root is shown here.
So we are interested to understand
and connect root structural architectural traits
and yield and other above ground traits.
And ultimately our interest is that we map this relationship
so we can breed for root to be straights.
So I like to wrap up in terms of just some of the learnings
I've given several different examples
of ongoing projects that directly
or indirectly help our breeding pipeline.
And they're expanding our toolbox
in new ways to expand the research output
we are now attempting to make our data
and codes public easily accessible
for all of our projects so that researchers can use them.
If there's an interest, this is a get hub account.
As new papers come that information on codes
and data should be available.
And then, well I've given
different examples in a short time
there are two important aspects I want to communicate
they're listed here
but two that I want to communicate are that just effective
and meaningful collaborations that are absolutely essential
for complex projects that some
of the projects I explained they're quite complex
and it's very difficult to find a single approach that works
for everything.
So there's no single approach that will solve
or work on each problem.
You understand these research projects are complex.
And what that means is that greater attention
to detail as needed with the continuous adjustments
and improvements with the methods and analysis.
And that will ensure that the outcomes are relevant
and applicable to the wider community of researchers.
And I'll say that there is so much more that we can do.
And what that means is plant scientist, engineers
computer scientists
if they can join hands on collaborative projects.
And with this I'll wrap
up by expressing thanks to the soynomics team
including the PIs staff, graduate students
undergraduate students that are currently in the group
and past team members, currently Brian, Jennifer and Ryan
a new employee, and just recently started,
there are staff members that help enable
these complex projects for our team
and sincere appreciation to the funding agency
the Iowa Soybean Association, United Soybean Board,
North Central Soybean Research Program,
Bayer Chair, Iowa Crop Improvement Association
and then support from USD and NISF
and all institutional support is greatly appreciated.
Thank you for your time.
Vikas: Thank you, Danny.
Thanks for that excellent talk.
One thing that caught my attention was you said
you talked about drought phenotyping and you said
there's now that infrastructure to do drought phenotyping.
And we kind of understand the challenges, right?
Because you have to have that doing draw phenotyping.
If it's in the field, it can be challenging.
And if that is more rain that season
then that can disturb your experiment and things like that.
So one question was about when you talked
about drug phenotyping
is it in the field and are you thinking
of like Reno shelters or something like that?
The second question along the same lines is if I'm not wrong
in same project, you mentioned about bypass dental families
and also screening germplasm accessions.
And I was wondering that
for genomics allocation kinship really matters.
And it depends it's the accuracy is sort of dependent
to some extent on that kinship
but with ML methods
my understanding is somewhat independent to that.
So do you see better results with drought rates
with by parental farm this was max accesions
or diverse accessions?
Singh: Yeah, thanks for those questions Vikas.
So, yes the study that I presented there
that is a field site infrastructure there.
And your point is a good point.
That dark screening is you may be successful
on the year but you may not be successful in other year
because rain may just you're just dependent
on the rain and rain are shelters were maybe circumvent
some of the challenges, but they are inherently
they have their own problem because some of the skills
that we want to use in a breeding nursery
or in our shelters would probably
not be an economical solution for us.
So right now we are still
at the mercy of mother nature
that we may get a a good year
where there is an extended period of no rain
that allows us to get some good quality data.
Both for DBAL stresses, but also DBAl stresses
there is a challenge to connect greenhouse
or control environment condition
and its translation to field.
And that's being shown over and over again.
So there's a lot of effort that still goes on
in order to make sure that we can have some more
maybe more easier, more controlled type
of situations where we can still get meaningful data.
And that's an active area of work
but to your point about whether it's in the genomic studies
or in the phenomic study, if there is the reason
for ensuring that we are accounting
for any dependencies is to ensure
that our results are not biased
because of any pre-existing dependencies,
whether it's in terms of a relationship
or maybe something else.
So when it, in our approach in the genomic side
of things, yes those things have to be accounted
for what are the animal side of things.
If there are aspects there's some ML tools that will allow
more intelligent sampling.
One of the examples I gave was that active learning example.
That's probably where you're referring to.
If you take a random sample
then random would serve as that baseline.
But if you're using some of those active learning methods
it they're using different type of functions
that allow them to pick the samples
such that they are able to sample all different classes.
Let's say it's a classification task.
And that gives the model better ability to perform
for the tasks that it is being generated for.
So it doesn't ML weren't necessarily removed
because if we are not accounting for those relationship
that model will probably be a little more myopic
in what it can do a good job.
And then outside of that, it will probably fail
because it has not seen that data where of course
they are some more advanced techniques
if you have a lot of data.
And so the deep learning methods
they may not have the same type of issues.
I don't know if that what you're thinking.
Vikas: That makes sense.
Yeah, thank you, George, please go ahead.
George: Thanks Vikas, hey Danny.
Thank you for coming.
Good to see you too bad.
You can't be here in person, but maybe shortly.
Thanks for the presentation.
It was really interesting.
I had a question related to the root imaging and root data.
Just wondering, just a curiosity question.
You were talking mostly
about architecture and that's really interesting.
I was wondering if you were able to evaluate any differences
in growth rate and if there's any relationship
in the rate and between ray and architecture.
Singh: Sure, thanks George.
With the time series experiment
with the Aria software that I presented
the current methodology only works deliberate 15 days
and we can make the those background paper
that we are currently
using to go beyond it, but it starts getting
into that question the cynic
and field breeders like yourself and myself, George
we start thinking how does it really relate
to what happens in the field?
So we are using that current, those sorts
of experiments to really understand what is happening
in the architectural traits.
And it's still in a 2D, it's a two dimensional
we tried the 3D and in the early stages
it does like it can work
in the later stages where it's very structurally robust
it works, but otherwise
it just becomes like a bluff.
And we have some difficulty
in getting known because there's a lot
of occlusion because it just becomes like a big glop.
But in the time series that we did
up until that 15 days, yes
we were able to determine the datas
for a number of traits, which gives us again
opportunity to at least start formulating some hypothesis
that maybe we can test in the field that certain genotypes
they have a differential rate in terms of their development
but then it comes back
to how do we relate it back to the performance of the plant?
So it's a very vast area, George
and I hope a lot more people can get
into in studying of those root traits.
Vikas: Okay, thank you, Jim, please go ahead.
Jim: Okay, thank you.
I had a seminar by the way, Danny
a nice overview of everything.
Appreciate your willing to take the time
to present this year.
I was amazed by one of your (indistinct)
were you able to detect nodules at the V1 stage?
And normally when we talk about in the field situation
they're very hard to see until about the V3 stage
and the V1 stage we go by fair and cavernous
where the the the first trifoliate is just unfolding.
So is your software that good
so that you can detect nodules almost
visible to the human eye.
I see where they want.
And I thought, man, that's pretty good detection.
Can you comment on that?
Singh: Yeah, Jim, thanks for making time.
It's good to hear you.
So these experiments, they were based
on two different sites that we used.
We did one of the sites we did
and all the care was taken to extract it
at these stages and carefully current
at every single nodule that could be farmed.
So I don't know why we didn't see much difference
at that V stage but obviously we did see some nodules
in this particular image based software.
They are able to like, if we can give the label data
then they are able to recognize it.
And it gives confidence that each
of the box that it draws automatically
that it draws once it's trained the model stream
it does give confidence level
whether or not the model fields that yes
I feel comfortable that this actually is a nodule.
We also, we were interested to see
whether there is any mixing up with cyst
soybean cyst for some reason,
and in the paper that hopefully will be coming
or we have covered that aspect as well.
So I'll have to look into a little bit more
the comment that they are not that if they're not found
at V1 but yes at least in the imaging that we did,
those were, the models did get
that data labeled data fed in into it.
And we also had every sample went through two people.
So one person did it initially and other person
did the the validation on it on the actual physical root.
And if there was a mismatch
then a third person counted again.
And then it was also done
on the image that was taken off the sample.
So I feel fairly confident that if it is counted
or annotated as a nodule I feel comfortable
that it goes it has gone through three people.
Jim: Yeah, I don't have any doubts about the thing.
I just thought it was kind of amazing because imagery
you can detect things beyond the human eye probably
but I can detect.
So you might be detected even just a budding amount
that it's pretty hard for the IOC.
And you answered my second question
about the SIS this seminar I've learnt very much.
Singh: Yeah, thanks, Jim.
Vikas: I want to ask a question
and this is probably unrelated to the research,
but just in terms of graduate education
I know that you've been interacting with students
and you're also the director for graduate education.
And just listening to the seminar today
we just talking about so many technologies
data-driven science, the Nomex
we still have the integral part of plant breeding teaching.
So how is plant breeding education evolving in academia?
And what are your thoughts there and will happen before?
Singh: Yeah, that's a deep, deep question
because I think there are some breeders
that are just, they're gifted
and George, a good friend of mine
he's one of those just gifted breeders.
And they're probably some of the best breeders
that are in the country and the knowledge
that field breeders bring that is something
that courses alone cannot get an up and coming breeder
to really get that length and depth of the knowledge
that someone like George Getty's.
So these courses are helpful
but spending time with the experts
it makes a lot of difference to the up-and-coming breeders.
And as they're using some of the tools
that George and I have some of the most routine tools
that we are using they're becoming more routine
like things with markers food selection
and even some of the drone based things.
But our students are also learning things that maybe George
and I are not that we're not experts in
but things that are just so new and they may come
from a non-planned science discipline.
And that means that those students
not only have to take courses in it
but they have to embed themselves
in those other groups to learn the nuances
of that particular discipline understand
get the hard skills associated
with that particular discipline, that particular topic.
And then also being able to integrate,
like it's one thing to get the knowledge
but it's another thing to be able to integrate
in some actionable outcomes.
And that's where the education component comes with it.
There may be a lot more cool advisee students.
They may have a major advisor
let's say I'm a major advisor
but then they have a major advisor who may be in college
of engineering or maybe in computer science.
So plant breeding is a fascinating discipline
because it integrates every other discipline in itself.
And that's why I think it's a great
I'm not going to say an easy
but I think it's just rightly placed
to get more interdisciplinary aspects in the education.
And that comes from the coursework, but also spending time
in learning with some really capable researchers leaders
and then also spending time outside of their discipline.
So I think it takes it takes a lot of effort
to have these breeders for the future
because some of the challenges that are coming our way,
I know, I'm not equipped to handle
maybe address those challenges
but I think the next generation of plant breeders
they will be able to, because they will be better version
of breeders than I can be.
Vikas: Sure, now that's a difficult question
hotline to answer but thanks for sharing those thoughts.
Thank you again for the great seminar.
We really enjoyed it.