Smart Farms and the Internet of Things for Agriculture

- Well, good afternoon, everyone.

Thank you for joining us today.

We're looking forward to our, "Smart Farms "and the Internet of Things for Agriculture" webinar today.

We're gonna get started here in just a moment.

Before we do, my name is Jim Ladlee, I'm the state program leader for Emerging and Advanced Technology with Penn State Extension, and I will be one of your moderators today.

The other will be Dana Ollendyke, our program manager, who you can see on the screen today, right now.

So before we begin, just a little bit of housekeeping and while people are joining the webinar today.

Number one, I want to thank all of our presenters before we even begin today, 'cause we really do have an all-star cast.

As you can see, we have Dr. Cherie Kagan, Dr. Long He, Daniel Dotterer from Daniel Dotterer Farms, Neda.

Loren West can't join us today, but he was a critical part of the conversation as well.

William Theile, Dana Ollendyke and myself.

I just want to introduce our presenters because they really do have a impressive connection to IoT4Ag.

I'm going to start with Cherie Kagan.

Cherie is the...

Steven J. Angelo Professor of Electrical and Systems Engineering, Professor of Materials Science and Engineering, and Professor of Chemistry at the University of Pennsylvania.

She is the director of the National Science Foundation Research Center, Engineering Research Center for the Internet of Things for Precision Agriculture, or IoT4Ag, and served as Penn Engineering's Associate Dean for Research from 2019 to 2024.

She graduated from the University of Pennsylvania in 1991 with a Bachelor's Degree in Materials Science and Engineering and a Bachelor of Arts in Mathematics, and earned her PhD in Materials Science and Engineering from the Massachusetts Institute of Technology in 1996.

Our next presenter is Dr. Long He.

Long is an Associate Professor in the Department of Agricultural and Biological Engineering and is physically located at the Fruit Research and Extension Center at Biglerville.

He received his PhD in Mechatronics Engineering at Yanshan University in China before joining Penn State.

Before joining Penn State, Dr. He worked as a Postdoc Research Associate Research Engineer at Washington State University and the University of California, Davis.

Next, we have Neda.

Neda is the CEO and founder of Microclimates, a leading ag-tech company specializing in innovative solutions for both indoor and outdoor agricultural operations.

With over two decades of experience spanning science, food safety, sales, leadership, mergers and acquisitions, and networking, Neda possesses the expertise to navigate the complexities of the evolving ag-tech industry.

With a strong science background, a Bachelors of Science, and an MBA, Neda combines scientific expertise with business acumen for the future of ag-tech.

Our next presenter will be William Theile.

William is a sixth-generation dairy farmer from Butler County.

He is known as "The Drone Guy," and you can find him on Facebook under the hashtag #DroneGuy, and he has taught many farmers about drone usage on farm and many other technologies.

He serves as the Pennsylvania Farm Bureau Board, on the Pennsylvania Farm Bureau Board of Directors and serves on the Pennsylvania No-Till Alliance Directors.

And then, along with William presenting, we'll have Daniel Dotterer.

Daniel is the owner of Daniel Dotterer Farms and was one of the first people to use sheep for solar grazing.

As a result, he currently has one of the largest sheep herds on the East Coast.

His family also has a long history in agriculture, as they have been farming continuously since 1720.

He was an early member of the American Solar Grazing Association and currently serves on their board of directors.

Daniel has extensive experience working in the LA entertainment industry, which greatly influenced his interest in augmented reality and connected farm management.

His return to the family farm offered him the opportunity to blend his vision for cutting-edge technology and his passion for the future of agriculture.

And then, of course, Dana and I will be your moderators and hosts today.

A few little logistics before we dive into the presentations.

This webinar is being recorded.

We do encourage questions.

There, if you notice at the bottom of your screen, you'll see both a Q&A button and a chat button.

We encourage you to use the Q&A button for any questions related to the webinar and the chat button if you want to make a comment or if you want to share a technical issue that you're having, and we'll try to help you address that as best we can.

We will also do our best to answer all the questions we can during the program.

However, sometimes time runs short, and we will send a response afterwards if a question comes up that we cannot address during the allotted timeframe.

For more information, you can also stay in touch both using our Ag Tech interest group, you can also reach out through the Center for Technologies for Agriculture and Living Systems.

We will also be continuing these webinars on Ag Tech conversations in January, and you will receive an invitation to those Ag Tech, since you registered for this, you'll receive one of those, or you can follow along at the Extension website for more Ag Tech webinar options.

Again, just a housekeeping detail, where we use trade names and other things, you can't talk about Ag Tech without using trade names, our vendors appear, but there's no discrimination intended and no endorsement by Penn State Extension is implied.

The presentation can be made available in alternative media upon request, so if you need anything like that, please let us know.

And just a quick thing about Extension, we're talking Ag Tech today and the Internet of Things for farms, agriculture, and food businesses, and natural resources, but Extension also covers, 4-H and youth development, agronomy and natural resources, animal systems, energy business and community vitality, food, families and health, food safety and quality, and horticulture.

At the end of the presentation today, at the end of the question and answers, and at the end of the webinar, we will send out a very short, it's five questions, so it's very, very short survey.

But the survey is super important for the future of these webinars and the future of the work that we're trying to do with Ag Tech and getting the information out there to the public.

So, please take a moment to both reflect on the webinar today and to offer your suggestions for the future.

It really is important to what we do.

You'll receive a popup at the end of the webinar, or we will also be placing the survey link in our chat.

So, you can use either the popup at the end of the webinar to complete the survey, or you can use the link in the chat at the end of the day, or at the end of the webinar.

Our goals today are pretty simple, we're covering a lot of ground.

A lot of really great people are covering really big topics.

So, we're trying to create a foundational level of knowledge about the Internet of Things today and some of the things that are happening out there.

We're trying to offer some updates on current IoT research, or at least an overview of current IoT research.

We have industry experience on the panel today, so we'll have some insights into industry applications of IoT on farms.

And then, we're looking forward to the practical perspectives from farmers on IoT technology from William and Dan.

And then just as an order of the webinar, how we're gonna be doing things, Our first presenter will be Cherie Kagan, talking a little bit about her NSF project, which is really fantastic.

We'll have Long He talking about the fruit and vegetable operations and IoT that he has experience with down at Biglerville.

Again, exciting information and exciting things that he's working on there as well.

Neda will be talking about the industry perspective and some of the dashboards and things that they have to integrate sensor technologies out there.

I hope you find that as exciting as I do, about how we can build these new dashboards and connect devices.

And then, super excited to have William and Daniel here, two early adopters in the farm community, looking to help us understand what the perspectives are from the farmers, and what they need to see out there, what they're excited about, and where their needs are.

So, really looking forward to that.

And then Dana and I will wrap up with the questions.

So, thank you everybody.

I'm gonna stop sharing my screen here.

And at this point, I'm gonna turn it over to Cherie, and she can start her presentation.

Thanks, Cherie.

- Okay, hopefully, I'll get this to render properly in just a second.

There we go.

Can you guys see the slides okay?

- [Dana] Yes.

Perfect. - Okay, great.

Trying to figure if I can hide this somewhere out of my way.

Give me one second, oops.

All right, well...

Give one second, I'm just gonna hide the floating panels.

All right, well, that'll have to do.

All right, well, wait, let me start.

So, I just wanted to first say thank you and that it's a pleasure to be here and participate on this panel.

I wanna say thank you to Jim and Dana for the organization and invitation for us to get to participate.

So I'm, as Jim introduced, I'm Cherie Kagan, I'm a professor at the University of Pennsylvania, but perhaps more importantly, I'm the center director of an NSF Engineering Research Center that's called the Internet of Things for Precision Agriculture, or we call "IoT4Ag" for short.

And this center is a partnership amongst universities, government, and industry.

You'll, I'll show you a little bit more about that, but I'll say that you'll see more and hear more about the center being a partnership between the University of Pennsylvania, Purdue University, the University of California at Merced, and the University of Florida.

And then we also have partners in evaluation and assessment from Arizona State University.

It's a center that was launched during the pandemic in September of 2020.

And something that we still have about five, six years of potential federal funding to continue to support the activity as we work to become a long-living and sustained center.

Like anywhere else, you can also find us online by many means as well, and we're certainly happy to have more partners to work with as well.

But let me tell you a little bit about the center.

The center's mission is to create and translate to practice Internet of Things technology for precision agriculture, or appropriate for today's topic, and to train and educate a diverse workforce that will address the societal grand challenge of food, energy, and water security for decades to come.

So maybe I'll tell you a little bit about how we look at this and what the NSF Engineering Research Center program is.

It's one of the, for engineering, it's what they consider their flagship, the largest center program that the National Science Foundation has in engineering.

It has what we call four foundational components.

One is in our research, which is what I'll focus on today.

We also have a lot of activities and programs in what they call workforce development and diversity and culture of inclusions, really to reach out to very broad people, to include them within the center and lots of activities that helps with education from K through 12 to university to professional education.

So we like to be able to interact with others and share the work of the center on ways that the technologies can be useful to others.

And then finally, I'll introduce you very briefly, on the last slide, to our innovation ecosystem as we think about how do you take research and how do you do this in a way that you can translate that technology to others so that they can put them to practice.

So that's really the goals of the center.

You'll see a little bit more as we think about how all of the activities, today I'm gonna focus on the research component that's represented by here in a little bit on innovation ecosystem, and I'll come back to how these all work together in practice, but I'll share with you a little bit more about just the big ideas of the center.

So, as I mentioned, the center spans four different partner universities throughout all of our activities.

And I just wanted to introduce you, you can see the team, in many cases here through our pictures.

I wanted to introduce you, it's easy to see as people, but what's one that's at these four institutions, we travel back and forth, every year we have an annual retreat.

And you'll see that all members of our center, whether it's our students, faculty, advisory board members, Jim's on our advisory board, all get to participate in these activities.

They're a really great way to bring people together to even further our collaborations.

And so we go to all of the institutions in different years.

We think that this next year will be at Penn, but we've been to Purdue, Florida, Merced, and then we finished, since Penn leads, we'll come about in the rear end and be last, but it's one that we really welcome and learn about all of the different...

You know, we're very interested in thinking about and learning from others about how the kinds of technologies and systems we develop could make an impact for different cropping systems, in the face of different regulations from different states, different climates.

And so this is something we think is a very important part of our education and our ability to make an impact and work with others.

It'll take lots of us, right, to solve this problem to sort of bring solutions, these IoT solutions, to thinking about smart farming.

So, we like to take this picture of what we think we wanna do, we think through this idea of Internet of Things technologies that we really wanna bring sort of what we consider the farm of the future.

And to really be able to introduce a number of technologies, I'll say more about them here, but these things that look like rainbows are supposed to represent sensors that could be distributed in the soil or on leaves and plants in our environment.

You'll see a big part of the center is in robots, often not just one robot, but many robots that can work to be able to directly image the field as well as distributed sensors, battery, and communication technology.

So how do we get all this data and think about operating at field scale to get the data from the field out to the cloud?

And ultimately, a component of the center is taking all of that data and then being able to create human-centered decision ag interfaces that really provide farmers with recommendations to help them control and manage the farm.

So we think about how to do this when we talk about these technologies and bring all of these technologies to build integrated systems.

How do we, all of them are necessary components, and we think about doing this for both row and tree crops.

So we're interested in putting the technologies to work to be able to address different types of crops and, as I mentioned, climates and such and such.

But the idea really is to think about how can we capture the data that's really needed to be able to do that with spatial resolution, temporally throughout a growing season, compositionally to measure different targets of interest and be able to develop one picture so that one could efficiently control the state of the farm?

Okay, so we take this picture, and I won't go through this in detail, but we translated if we want the picture, this a picture of the farm of the future, how do we translate that into the work that we do?

So we can retell this story.

We, some of us like to call this a flow diagram, some of us like to call it a wiring diagram, because we have lots of different colleagues with different expertise who are part of the center, so this includes agronomists, agricultural engineers, plant pathologists, horticulturists, as part of the center, as well as folks who are physical and cyber sort of engineers, right?

And so, we bring everybody here together and they work together, and we think about how do we, to do this, we sort of structured our work into three thrusts.

One is on agricultural sensor systems, which I'll introduce you to a little bit more in a moment.

That's color-coded blue in the wiring diagram.

Thrust Two is on communication and energy systems, which is color-coded in green, and Thrust Three is on agricultural response systems.

You can imagine that we need to really, you know, our agronomists work with our engineers to teach us what are the stresses that we need to be able to measure.

We work together to build sensors that may or may not have power, but definitely need to be able to communicate their signals out.

Robots that can then measure these sensors as well as the fields, they need power and communications, and they can even do some data analysis sort of on the fly.

And then how do we take that data out to the cloud where we're looking to build AI digital twins of different cropping systems so that we can take and fuse and put these data together so that ultimately we can develop the controls and interfaces so that we ultimately provide a friendly interface that can be used to make these kinds of recommendations, always putting to work the idea that our technologies are only useful if they're adopted.

So we think about how we make that work here in the center.

So, one thing I'll say, as you look at research, and obviously IoT can be used in different sectors, really one of the key distinguishing components of the work of the center is that we're really driven by the agricultural-specific use case of those technologies.

So we think about the scale at which the technologies have to work, the environments that it works, and also the socioeconomics to ensure that the technologies and processes, the way that we use them, meet the needs of the farming community.

So you'll see we're working on building in sensor systems technologies that allow you to measure different targets of interest that would be low-cost, distributed, environmental, and soil sensors.

We work on aerial and ground-based robots.

In Thrust Two, they're looking at energy storage and delivery technologies that'll let everything work at the scale of fields.

Our colleagues are working on ag-specific edge and backhaul communication technologies.

How do we get the signals out in environments where, in rural ag, where you might not have, we don't have necessarily broadband communications?

And then we look at building AI digital twins of row and tree crops and then that interface that really helps with field management.

And so I'm just gonna run through these just quickly to give you a sense.

And in Thrust One, we really wanna think about how do we get the data and be able to collect data from the field.

So if you think about what our sensors are doing, I'm just gonna give you a perspective on, Jim said, you know, where are we in research.

We look at building both emerging technologies, things that you won't find.

Examples are, is that we're looking at building little color and metric sensors that could be deployed, for example, on leaves.

And they would change their color for in the presence of a target of interest.

We are also building soil sensors that can be buried in the ground.

And the idea is to do this that they'd be low in cost.

These would operate at radio frequencies so that you, 'cause you can't see below the ground.

But the idea is to build these different types of sensors that are low cost.

The ones that are biodegradable, using materials that are biocompatible so that things that you would think about would be fine in the soil.

So we think about how do we, or on the leaves, so we think about how do you do this to, to meet the needs of agriculture.

And so we're interested in looking at how do we meet all sorts of targets of interest that could be, you know, to understand water stress, temperature, volatile organic compounds, nutrients, what happens when there's something that's not good, like pathogens and pests.

And then we look at how do we take and build these leaf in soil sensors, but also equipped robots with various different types of sensor technologies that allow for their navigation, for their imaging, and even physical sampling, as you can see here with an arm of samples from the field.

And this is then married with some more virtual assets.

So that, how do you know, like for example, if you have a sensor, a robot will navigate, identify that the sensor is there and then do the measurement for example.

And then how do you also deploy these in a very effective way, so we have folks working on planning algorithms to know how to effectively distri...

you know, use the robots in the field.

The next is on communications and power in rural ag.

So just to give you a sense, we work on physical assets like equipping large machinery with communication tools that are sitting here on the bottom and could read and sensors in the field and even transmit power if we needed them to.

And also nodes that are actually, you know, simple ones, they're actually fairly low in cost.

We even use them for education and outreach.

We have one at Penn that we're starting to do is we just work in the community in Philadelphia.

But these ideas to be able to get, these are based on rural wanting to get the data, you know, from the field and then out to the cloud.

So we think about how do we do this with both physical assets that we're building as well as, you know, virtually so that we understand and think about, you know, how do we have appropriate communications and security to be able to do and operate in rural and remote areas and to introduce these networks.

And then, finally, we think about how do we take the data that we have and do analysis on it so that we can translate that data into what we think of as knowledge and intelligent action.

So, I like this depiction because you can think about all kinds of sources of data that could be collected from the field.

These could be from sensors, they could be from robots, it could, it also includes, for example, public data like weather, for example, GPS data, all sorts of things that can come together.

And that these data then are labeled so that we know how to sort of put the pieces together and build a full story so that you can then take all that data, build these digital twins of cropping systems, use machine learning, but that's constrained by biophysics so that we do it for the context of the kinds of crops that we're doing, and then to create those interfaces, for example, one that's shown here, that is friendly so that you can effectively manage the field.

We do this, and I'll say that I'm at the University of Pennsylvania.

We're the lead institution, obviously, we do not have, we're excited to work with others, we don't, obviously, we don't have large agricultural fields and...

and extension.

We're happy to work with Jim and Long, for example, but also we have extension within our partner institutions at Purdue, UC Merced, and the University of California, sorry, the University of Florida and its various research and education centers.

And so we look, this gives us a really nice span of different types of cropping systems for us to test in our systems and to make sure that we can meet needs of different crops.

We also do this in a way to try and bring together all of our technologies.

So we wanna build these integrated systems.

It means that we have to take technology, all sorts of projects have to work together.

And so we created something called Joint Ops, "Joint Operations," so that we could both structure us to build, bring those technologies together, but also to sort of use it as a way to tackle important challenges in agriculture.

For example, we have joint ops on optimizing nitrogen application in row crops, water use in tree crops, and a newer one on how to mitigate disease and pests.

So, maybe in the interest of time, I'll skip through this slide.

We try and bring all of these technologies together, and you'll see these little pictures or thumbnails that just show how they get integrated together to build those integrated systems.

One very important part of the center is our innovation ecosystem.

Our innovation ecosystem consists of industry members.

I'll say they are fee-paying, I'm happy to share more with that, as well as non-fee-paying members that really represent the end-use farming community.

You can see Jim's picture over here, 'cause he's a member of our center, but you can really see we have a number of companies that span technologies to integrated systems to the end-use farming community.

The idea is everybody gets exposure to our research, our IP.

They can serve on projects and give feedback on them.

They can come to all of our meetings, workshops, seminars, and really also have access to very talented students and postdocs.

So with that, I'll stop and just say at IoT4Ag, we like to say we wanna be delivering the people, places, and internet of things to enable a food, energy, and water secure future.

Since I'm out of time, I won't say more, but we hope to make this depiction here a realization.

And if anyone is interested in joining IoT4Ag, please feel that you can just capture a snapshot here of a QR code, or you can also find us, if you just put IoT4Ag, you'll find IoT4Ag.us, you'll find us, and you can always reach out to me, and I'll also be very happy to talk with you further.

So with that, I'll stop, and I think next my job is to turn it over to Long He.

- Thank you, Dr. Kagan.

- Yeah, I'm trying to stop sharing.

Come on, there you go.

- Thank you.

I'm sharing my screen here.

I think my screen is up, right?

- [Dana] Yes.

- Okay, great.

Yeah, thank you.

Thank you, Jim and Dana, for inviting me for this webinar.

So today, I'm going to talk about some of the Internet of Things for food and vegetable operations, especially some of the research projects that we, our group, has been working on in the past few years.

So first, I want to introduce the IoT into this big picture that we are actually having.

We are working towards developing an integrated precision agriculture systems, especially for the specialty crops.

And then integrated Internet of Things is actually a very important part of that.

And then also beyond the Internet of Things, we have involved with artificial intelligence and also automation.

Specifically, we can also involve robotics as well for this whole integrated system.

And then we combine multiple technologies, like sensors, communication technologies, AI models, data, and robots and robotics together.

So trying to put everything together, then we can work on the crops, production and work towards other kinds of ecosystems.

And we provide solutions for the end users, and also, our long-term goal is to benefit the general public for health, for the kind of public beneficial.

And...

So...

Next, I would introduce a few projects that have been working on related to the IoT and also some of the integration of IoT systems.

And then the first one, talk about the irrigation.

Actually this is the one, the first project I came to the Penn State and then start working on this.

And we actually using soil moisture sensors and some other kind of like data loggers to collect sensors with, with soil moisture, and then to provide suggestions for irrigation.

This is just a picture showing how we install the sensors into the orchards, and then with the data logger and communication, we can actually access those data remotely.

That is for the benefit of using Internet of Things that we can actually access data with our like devices like phone or computers, so we don't need to go to the field.

So this is kind of the first step, we're using IoT for monitoring the crop of soil conditions provide kind of recommendations for production.

So we are using this for irrigation.

And then we actually did this work with a few growers in the region and then install sensors and then the systems into their farm and help them to connect that soil moisture data and then guide for irrigation.

So those are some pictures showing that, and then we're trying to take this one step forward.

So, because this is still just for collecting data and monitoring the soil moisture, but also we would like to see if we can automate the irrigation process.

So we are trying to add more components into this IoT system, and the one thing we add on is the valve control, and that also using...

Using the LoRaWAN, is one of the IoT techniques that using IoT platform kind of combine everything together so we can have both soil moisture monitoring and valve control for automation as well.

So this is one of the study we did in a peach orchard here at Biglerville, and you can see that we have sensors, actually underground here, and we collect sensors into this control box, and the control box also connects with the valves and other sensors together.

So everything is integrated here, and this is one of the interfaces that we had developed, and then we can, in real-time, monitoring the soil moisture in the field, and also we can set up thresholds for the irrigation as well.

So if we set up one threshold for one level, soil moisture for irrigation, the valves can be automatically triggered on to start irrigating the block.

And also we can look into see how the soil moisture changes over time and see how we actually did working for like, how well that we integrate this block with the whole system.

And then we also bring this system into vegetable fields.

This is an open tomato field.

And also we worked a little bit on the vegetable like in the high tunnel and the greenhouse, trying to help for irrigation using automation as well.

And those are like we bring the automation into the IoT system, but also later on we thought about how about we bring the artificial intelligence into this system.

So here is kind of a....

structure that we actually develop a AI and IoT combined system...

for growth monitoring, and you can see we have sensor layers, transmission layers, and application layers.

So those are the three layers actually combined together with Internet of Things, so we can actually, using the camera system, monitor the crop growing conditions.

And then we have the AI models help us to analyze the crop growing condition and other things.

And then we can integrate this into the IoT that for us to do more further data analysis and also for crop, for the operation as well, for example, for irrigation and nutrient management.

So, this is just a structure of this idea, and then we can implement this into different things, and we start with a greenhouse vegetable monitoring.

You can see from this picture here we have, this is the experiment set up here.

We have a camera on top of the vegetables, and then we can take images as the setup.

You can pre-design the time period that you want to take an image, and then you can upload through the IoT.

And then those are the controller microprocessors and other sensors and controllers to collect those datas together.

And as end user, we can actually access those data from our computer and then further process the data to have a better idea like what the crop growing conditions are and other kinds of...

like water or nutrient level in the system.

So those are that.

And AI play a big role here, is using AI, like using AI.

I mean here it's more like using deep learning kind of models for...

for crop monitoring here.

You can see here, just three different models we used for this example.

And then you can find the last one here is called the Recursive Segmentation Model.

It's just one of the AI models that we used.

The idea for this one is we are trying to find each individual plants and the under-leaf area of each individual plants along the growing stages, so we can better predict the growth of the crops and also predict the time of harvesting and the yield along the time.

So we found that this, actually this, we improved this AI model and get a pretty good results on that.

So this example is not just trying to indicate that we can use AI and IoT combined, integrated together, to help us make better decisions for crop operation.

And in the next I want to introduce another project.

It's called Integrated Solutions for Crop Load Management for Apples.

And then we have been working on this on different aspect, actually here, just to give a few examples.

We actually started from pruning and then, and then flower buds identification and then have the like blossom thinning and then green fruit thinning.

And then, and oh, oh, oh, we...

for example, for the blossom thinning here, you can see that our goal is to...

to identify the flower numbers in a tree and then trying to using a more precise spraying system to apply chemicals to those flower clusters.

And then in order to save chemicals and then, but also perform well for the crop thinning.

Crop thinning here for blossoms is we are trying to apply chemicals into flowers and then to minimize number of flowers to develop into the fruits, so we can reduce the crop load at this stages.

The first step you can see we using AI here to actually, to identify and localize flower clusters in each, in a tree canopy.

And then once we have those informations, we can have this, this is a robotic prototype that we develop for thinning.

So here is a nozzle, you can see the nozzle actually there.

So we can spray chemicals to the flower cluster we actually identified, and here's just a one short video showing that as the flower cluster was identified and detected, we will spray chemical thinners to those flowers.

And then next example is we also worked on the green fruit thinning.

Same kind of concept, we first actually, you can see at this fruit apples at this stage, like about 20 millimeter stages.

If you still have a lot of apple fruits on the tree, we need to remove some of them.

So that actually is the last step.

We can adjust the fruit, the load, particularly for a cluster.

For example, here you can see one cluster has four fruits, that's actually too many.

And we need to remove two or three of those from the cluster and then to remain one or two.

So that's a goal to reduce some of the fruits' numbers on the tree.

And then, like the general process, we are using AI to identify fruits, and also here we actually identify the fruit stems and other branches along the way so we can better to using mechanical or robotic system to positioning to each individual fruits and to remove them.

This is a, the robotic system we developed is a, you can see for the end effect, for the end part here is kind of cutting mechanism we actually engage into individual fruits and remove, cut the fruit stem off for those fruits that we don't want on the tree.

And for this, this is the last slides I have here is a, it's also for crop thinning, but it is using kind of chemical sprayer.

And this study is a trial thinning that, field trials, that we actually worked with two companies, and this is the Vivid Machines is machine vision company and another company here is using H.S.S. sprayer, so, for the spray.

So, I am here just trying to see how the IoT and robotic system maybe can come together.

So you can see that once, once the imaging system kind of take images for the whole field and we can actually generate some the like crop load maps for each individual tree and you can see how many actually crops in the each individual tree.

This actually is a process Internet of Things.

You can upload this into the cloud and then process the data and come up, come up with this kind of crop load map.

And based on the map, we can actually provide kind of a spray map to the sprayer to spray chemicals to the tree canopies to more precisely, if we have more fruits or more crops on a particular location, we can spray more on that location.

Those just like closed-loop control system here.

So that's more like integrated system.

So I will stop here and then just thank you for my group here and then some funding agencies.

Okay...

So I will, I'll ask question later, but I will turn it over to Neda.

Let me stop sharing.

Neda, please go ahead.

- Thank you so much.

All right, I think you can all see my screen now.

Thank you so much Dr. He, and to the, to both Jim and Dana and Dr. Cherie Kagan for the previous presentation.

There's been a lot of talk here in regards to IoT and integrated systems.

So the focus of this presentation is gonna be on profits and peace of mind making technology work for your farm.

I'm the CEO and founder of Microclimates, as Jim mentioned.

We are a tech company in the software industry really focused on integration of systems, so disparate systems.

We provide an integrated platform for environmental monitoring and automation, and we've tried to really work on making affordable, scalable, and a sustainable platform.

There's a huge growth in IoT, as you've heard from the previous presenters.

We have had this massive growth in IoT, especially in the last three years, where we have IoT sensors for pharma, specifically that have grown by about 40%, and there's more than 200 companies that really launched in the sensor for ag, which is really working in our favor in the farming industry.

And about today, about 27% of US farmers have integrated precision ag technologies for monitoring and automation.

What does this mean to all of us?

It means that we have lower cost and bigger benefits.

So some examples I wanted to provide is the fact that, you know, soil moisture sensors that are available today.

A couple of years ago, you know even three years ago, they were $300, and they dropped to about $100 now.

Outdoor weather stations we used to see for $3,000 to $5,000, they still exist today, but now we have solutions available that are under $500.

Energy monitoring units over $2,000 at its circuit level behind the panel are now at $150.

So there's a huge savings to farmers.

We're really beginning to see a lower hardware cost, easier-to-start and scale solution for farmers, and wireless technologies are flooding the market.

And these are the LoRaWAN Long Range Area Network technologies, which really just means more tools for you farmers and companies, which means better choices that you can make for your farm.

Now there's some benefits and there's challenges to implementation, and what I'll be talking about is the real case scenarios and what we're seeing in the industry with some of the benefits and the challenges.

From a benefit perspective, an example is, obviously, precision agriculture.

A perfect example is precision irrigation, where we can now implement at, you know, a few hundred dollars, an outdoor weather station at a farm that can measure rainfall or rain intensity, temperature, humidity, wind speed, gather all that information, along with real-time soil conditions, pH, temperature, and moisture levels, and then really reduce the water usage.

So you have this smart irrigation system, precision irrigation systems, we can reduce the water by 25%, and it can also help maintain high-quality crops.

This provides, from a benefits perspective, a peace of mind.

What our clients tell us is, you know, "Before I go to bed, I pick up my phone "and I have peace of mind "that my operation is running smoothly." Then you have alerts and warnings and critical alerts and warning measures set into place.

You obviously have data-driven decisions that are made because if you have an integrated platform, a system that's hardware agnostic, then you can go ahead and integrate as many data points as possible.

So now you have a lot of data points that are coming together on one platform.

You're making decisions based on data, cost savings, and reducing your labor and energy expenses, and more sustainable solutions, and then obviously time efficiency, where you have less of your employees walking through the farm and looking at all these data points.

There are some challenges, of course, when it comes to the implementation of these IoT devices.

One is connectivity issues.

There's limited internet access in rural areas, so we always tell customers, you know, you wanna look for partners that have a considerably flexible system.

We work with a company called Via, for example, they have a platform that can connect to Starlink.

It also allows for LoRaWAN communication.

So that's one of the things that's important for us.

And Cherie kind of touched on edge computing a little bit.

Edge, for anyone that's not familiar with Edge, I just, at least I saw it in the slides, Edge is essentially the work that's being done on-premise.

It's not going into the cloud, it's been, the computing is happening on-premise, and that is something that we offer through this Via platform, Edge computing on-premise, and the data can go into the cloud, but for now, it's owned on-premise.

From other challenges is LoRaWAN, is IoT sensor technologies, but there are solutions to that, such as the LoRaWAN technology, which is long-range area network communications.

And so many of these sensors can really communicate up to the size of football fields, and they can have battery lifespans at about five years or more, so it's really helpful.

There's some cost concerns, obviously, upfront costs for any of these sensors and devices.

But I mentioned a lot of pricing is dropping 'cause there's more and more newcomers on the market, which is working in our favor.

Sensor placement, tractors, and equipment can run over them, accessibility can be an issue, so you really gotta be thinking about conducting a field survey to understand where these sensors should go.

And there's a lot of work that's being done, again, in the sensor world.

So we're gonna see more and more sensors hitting the market.

And the data ownership is also important.

Who owns the data?

Do you feel secure with the data that you have in place?

But a lot of these, what I'm showing you on challenges, we're really beginning to slowly address those, and we're seeing some great progress in the industry.

Upfront cost versus subscription is another concern.

It's actually one of those things where all of us in the farming world have really been used to paying one-time purchases, and it's been upfront.

Some of that is changing slowly with more and more software companies hitting the market, where you may not be paying those high upfront costs, but you may be paying on a monthly basis, which is referred to as a software as a service model.

And these can be affordable and predictable versus large upfronts.

Some of the benefits to that is a remote support that you receive, continuous improvement, you wanna be working with companies that provide over-the-air updates.

So some are similar to your cell phone, where you wake up one morning, and all of a sudden your cell phone has got new features because you had an update.

You want the same thing for the platform that you choose.

You want the ability to have a system that can be upgraded over the air.

Some things to think about is data backup and security.

We touched on that a little bit as well.

Is your data on-premise, is your data going to the cloud?

If it's going to the cloud, security aspect.

If you don't wanna go to the cloud, can you have the data on site?

You sure can, but when it comes to the world of machine learning and AI, some of that computing, 'cause it's large data, is gonna have to go into the cloud.

So you wanna work with companies that really understand data security.

Is the system accessible from anywhere, anytime, and is it flexible for you to scale?

So SaaS companies are really incentivized to keep systems running well because that's how we actually make our money.

So we, it is a long-term partnership, but again, it's a new way of thinking about spending money when it comes to, when it comes to these IoT technologies.

So, as you're thinking about choosing a system, some of the things that are important, and I saw these in the previous presenter slides, a lot of conversations regarding integrated systems or integrated platforms.

So, having a hardware-agnostic approach is always best for everybody because then we get to integrate all these different systems so they can all speak to one another.

So choosing a platform that works with any brand or type of sensor, it's also going to avoid any kind of a vendor lock-in and an easy-to-use interface, something that makes sense to the end user, and you can use your, and you know, it's easy to manipulate and set up alerts and get systems to speak to one another and look at data as a day-to-day user.

So the benefits of choosing a system that gives you all that obviously is better prices, better fit for your farm, exactly what you need for your farm.

The ability to be adding on new sensors that are just emerging technologies, customizing your dashboards, and again, over-the-air updates is something that I already touched on.

The value of data is also very important.

The value of data today, and I think this is one of the questions that was asked, and we can touch on this.

These digital twins, for example, the digital twins collect a lot of data, and that data can be used to inform a farmer today on what to do.

It can provide insight, immediate, real-time insight.

It can also provide insights in making decisions based on data.

But what's really important about collecting data is that this data needs to be collected so that we can be prepared and we can start working towards more of these AI and machine learnings and the other technological advancements that we're seeing in the market.

And so with that, the data being collected at your farm, just know that if it's not collected, it's gonna make it more challenging to customize something for your farm.

The real impact is really increasing yields from 10 to 15% based on real-time data.

And there's some studies out there that's showing that today.

So there's a huge value in just collecting data and having that data today and for the future.

I'm gonna run you through an example of a farmer's journey.

Hearst Greenery is one of these clients.

Mr. Blake Hearst is third-generation farmer, and his farm includes greenhouses, soybean and cornfields, and grain bins.

We started the journey with this specific farmer for just adding temperature, humidity, and CO2 sensors and helping him with his greenhouses specifically for AC and heater controls.

And that provided a huge savings for him, both from a labor perspective and energy perspective.

Then as we're, we met up with the farmer again and said, "How is everything going?" He said, "One of the things I'd like to have "is a better understanding of my field." So then we went ahead and very inexpensively, these outdoor weather stations, I think they're like three or $400, launched one from one company, launched a soil moisture sensor from a different company, and we installed those soil moisture sensors and outdoor weather stations so we can give him real hyperlocal weather understanding of his farm.

And that was the next launch.

As we're driving around on the farm, we said, "Hey, what about those grain bins?

"What do you do with the grain bins?" And he said, "Hey, we like to have temperature, CO2, "humidity, distance level understanding, "how much grain is in my grain bin, can that be done?

"It's a real hassle for me to drive to all my grain bins "and turn fans on, for example, "create a closed-loop system." And we said absolutely.

So that was our last launch with a specific farmer, and it's really created a sort of an integrated system with all these different systems coming together, speaking to one another, and making decisions under one platform.

I'm happy to answer any questions after this session.

I'm gonna pass the mic on to both William and Daniel.

Thank you so much.

- Okay, thank you, Neda.

So, my name is William Theile.

I am a dairy farmer from Western Pennsylvania.

And, like Jim mentioned earlier, I am the "Drone Guy," and that's one of the things that I do is do a lot of work with drones.

And one thing that I've been looking at, just by the presenters already today, is that smart farming has to be profitable farming.

So, all the awesome tech stuff that's been mentioned and the sensors and everything, it all has to be profitable for a farmer like me.

And so I'm a dairy farmer, and I do a lot of sustainable farming, I do a lot of drone stuff, of course.

Whenever we, as farmers, we see stuff like this and we say we want our farms to be profitable and it needs to be easily understood and affordable for us.

And Neda mentioned it perfectly about how more affordable, a lot of the sensors and all that tech, is becoming, and I see that trend continuing, of course.

And so, younger farmers, like myself and Daniel, we're gonna be adapting more and more of that, and our children are gonna end up adapting more to that, so it's gonna be something that's gonna be here and be here for a while.

So it needs to be adaptable to not just a dairy farm like mine, but all these different farms, it needs to be adaptable to us and it needs to show profitability.

And so, so that, so showcasing like, for instance, my conservation practices that I do, I like to know what my organic matter is in my soil, and I want to know all that stuff, and I want to prove to myself, basically, that it is working properly.

And so, those things that everybody else has mentioned would work very well on a farm like mine.

So, with drone technology that I implement on my farm, there's a lot of images and videography that I take, and all that data needs to go somewhere, and it needs to be used by a lot of the data points we've been discussing.

And so, I want that data to be secured, and I want it to be in a safe place.

And so we, obviously, that's been taken care of here.

So, but I, I want to, I also want to point out about the internet access, I believe that Neda mentioned, was that I know in my area I'm pretty good with internet access, but that's not always the case with a lot of farmers.

And so that's something that needs to be improved with a lot of farms as well because they don't have internet access, and obviously if you don't have that, you can't implement a lot of these things.

So that is, that's something that's important for a lot of farmers and with my drone, you know, I can use that data to do precision agriculture, to do my precision spraying, if I choose to do so, my precision planting, if I choose to do so, and all my harvest data that come from the yield monitor on my combine, and I wanna showcase where the good spots are and where the not-so-good spots are.

So, all that data can go to one area, and then we can learn about all all the places on my farm that are doing good and not so good.

And so that simplicity and that affordability can help my bottom line, and it can, it can do a lot of good to show all the things that these sensors and everything are doing to help my farm's bottom line, because I'm a relatively smaller farm than most, and every little dollar counts.

And so, being on something like this can show the affordability on not just a small farm but medium-size and larger farms.

And so, that is something that a lot of farmers should be looking at, especially in Daniel and I's age range.

And so...

affordability, simplicity, all that stuff is...

is gonna help us in the long run to improve our bottom lines and to be a smarter farm that is also profitable and to do a lot with that.

So, I know there's questions about security on your data and all the data that you can collect, and so we've obviously talked, we're gonna talk a lot about that as well, about how we can secure that data and to keep it within my farm, 'cause obviously that data's important to me.

And so I wanna, I wanna keep that data safe and to make sure it doesn't get in the wrong hands 'cause we know where that can go, and so, yeah, on my farm, I'm continuing to learn more and more.

It's not something that the faint of heart should be looking at, of course, but nonetheless, from a farmer perspective, doing smart farming is definitely profitable farming.

And so, Daniel, if you're ready, I'll pass the baton over to you.

- Cool, thank you, William.

Again, thank you to everyone that has gone before me.

I'm excited to be part of this conversation.

Yeah, with, you know, we, again, to remind you, we have a sheep operation here in Pennsylvania, and sheep don't take as many acres as something, say, like dairy does.

You know, smaller farms, we have to be more concerned with profitability.

We don't have those margins to really play around with.

And that's why things like technology is really important in data-driven decisions.

How do we take that information?

How do we make it easy to use, easy to digest, and quite frankly make easy decisions?

You know, I've talked to a number of individuals that take notes on scraps of paper and pieces of paper, and then those get put in a drawer and never get looked at again.

You know, honestly, those don't really do much good for anyone.

You know, things, you know, in livestock, we talk about rate of gain, and which of those animals that, whether that's gain, you know, a tenth of a pound a day, a quarter pound a day, half a pound a day on that same amount of feed, how do we make those decisions to, you know, make those animals more profitable?

Which ones are gonna stay on the farm 60 days or 90 days or 150 days?

You know, if we can get that same outcome in a fraction of the time, you know, those are the animals that we need to keep for breeding and keep those daughters in the herd.

You know, as we said with profitability, you know, it's really important.

You know, the margins are, are fairly slim in agriculture.

Is that time and energy worthwhile?

We're seeing a positive effect with that.

And a lot of issues that, you know, here in Pennsylvania, we're facing are, you know, issues of connectability.

Not only do you have Wi-Fi on your farm, can you even get a cell phone signal on your farm?

You know, those are all areas of concern, and we're finding more and more ways to address those issues of connectability.

And it's adding...

Also with that information, you know, we can get more, more information to pass along to the consumer.

You know, that traceability aspect, things like carbon footprint, things like, you know, again, whether it's poultry or sheep, or what have you, of whether it's antibiotics, organic, all that information that are important to consumers.

You know, you can really pick and choose, you know, how you want your, your food stuff to come from.

And I think that's important, especially for small and medium farmers.

You know, so much of today is driven towards bigger, bigger, bigger, bigger.

And I think we need to have a goal to have more people involved in agriculture, more people involved with this industry, and everything from the diversification of our food systems to just, quite frankly, general knowledge of agriculture.

So, in our operation, it's things like rate of gain, it's things like how do we keep, you know, some things as simple as record keeping, which a lot of, quite frankly, farmers aren't doing.

And many farmers that are using record keeping, it's really incomplete.

You know, I know there's a number of larger farms that just track things like animal behavior and really don't do anything else with data, with even parentage or, quite frankly, with number of offspring, because it's just too difficult.

So how do we make that easy?

How do we make, how do we really raise that floor...

for our farmers and everything from growth to, you know, even issues of health.

You know, how do we use technology to help catch those sick animals quicker?

You know, the sooner we can medicate and/or isolate those animals, the better results we have.

You know, they say it takes decades to kind of get that eye of walking through a barn and being able to identify that animal that, you know, there's something not quite right there.

So those are some of the areas that I think are very valuable in technology.

And again, it's whether that's a small farmer to the largest, I think it's invaluable for all of us, especially those small/medium farmers who really have to be concerned about things.

I'm not quite sure who I'm passing it on to next.

Jim, is that you?

- Back...

First of all, just say thank you to all the presenters.

As you can see, there's just a wealth of knowledge on the panel today with expertise across the spectrum of IoT for agriculture.

So with that, I think we have a question from the Q&A, and I think Dana is gonna throw that out there for the panel.

- Thank you, Jim.

So we have a great question here from Robert Mowery.

"Is there a minimum or optimal farm size "that is needed to realize the ROI, "and what is the average timeframe?" - I can answer some of that and pass it on to the other folks here.

There's really not a minimum optimum farm size.

It really depends on what's important on your farm.

So, what is costing the most, what is putting your farm at the highest risk?

Daniel talked about, you know, the health of the sheep, for example, that's important.

Then how do we measure the health of the sheep?

So it's really about understanding what is most important to you on your farm.

There's not a minimum.

There are so many sensors available on the market.

My recommendation is always to start slow and small.

Start on what is most important to you.

Start small, see if you realize some ROI without making some huge investments.

And then once you've sort of felt comfortable with that, you move on.

A lot of times we get a lot of folks to come to us and say, "I want the whole thing automated." And yes, that is the ideal world.

We want everything automated.

We wanna be in the Bahamas watching our farm operate.

That's just not the way that it really scales.

It's better to scale small, begin to monitor, understand your farm, understand what you're trying to do, slowly moving to automation and gain confidence with the system before you kind of walk away.

And once you do that, I really think that you could move into a more automated world with more data.

- Does anyone else have an opinion or experience they'd like to share?

- I'll just chime in.

I would agree with what Neda was saying, especially with some of the work we've been doing on Daniel's farm as an example, starting small and figuring out the automation piece of it.

Figure out how the sensors work, how everything comes together.

It takes time to figure that out, and so doing things that you're comfortable with, solving the bigger problems, trying to work on the bigger, or the bigger issues that you're having that will be the most benefit to your farm is really the goal.

So I think that's a really good answer.

Long, I saw you were typing into the chat for the question about using the LoRa to aid in robotic navigation.

Do you wanna take that one?

- Sure, I can start it.

I mean, I look into the question.

I think it's a very good question.

I don't really have very good answer to that because right now for most robots' navigation, we are using GPS, or for more localized, we're using like LiDAR sensor, those kind of things.

Using LoRa for navigation, I think there is some kind of, some kind of try kind of there, but then the accuracy could be very, very...

not that good yet.

At this moment, the technology, I...

just looking that, that the, the accuracy could be 100 to 200 meters.

So it's kind of like, like the, the technology isn't there yet for the navigation part.

But more like if you want to have, if you can use this LoRa for localizing some device in the field, if you can accept that kind of range of ag arrow, that could be usable, but I think for robotic navigation, LoRa is still not quite yet.

So, I just my thought on there.

- Maybe I'll add to Long's comment.

I agree with him completely.

I guess, I was, I almost thought about the question in the reverse way, that I wouldn't, you know, we don't necessarily choose LoRa as the way to guide our navigation.

I think that you'll find that there are other tools and communication tools, whether it's GPS or developing navigation tools.

And even if you didn't have GPS, that there are technologies that are coming along to enable those that'll probably perhaps serve one better, and that actually the connection to LoRa is sort of part of a whole network of thinking about how do you have, you know, distributions of robots, at least for us and different communication nodes.

Could be LoRa, we also work on thinking about, what else, if you didn't have LoRa, I was thinking about Daniel's challenge, right?

You know, we where even if you don't have software, we think about how do you get signals to hop from one, you know, station to the next, right, without having to have all that connectivity.

But we think about how it becomes part of the solution and the communications of, you know, robots can communicate with the LoRa as part of the network of getting data out.

- Just a general question, we talked a lot about kind of the research that's out there, what's happening currently in the industry, the rapid growth in the last several years of sensor technology and precision ag and investments in precision ag.

What do you see as the next evolution of IoT maybe over the next five years?

What do you see coming down the road that farmers or ag businesses or natural resource businesses should be aware of?

What's next?

- I could share what we're excited about is, you know, I was even looking at some of the other questions that were in the chat, right?

Like how do you have, I think someone asked about nitrogen, phosphorus, potassium, and calcium sensors, and if I think about, and look at some of the solutions you can connect to data, and I think Long had a couple of examples of electrochemical or ion selective electrodes that, you know, you could go in and you could measure locally.

I think I saw that with your pH and they're similar to pH, you can measure other ions like the ones that you would measure.

But, you know, one of the things that we're excited about, which is still in the research realm, but I'll say that there's a lot of research, for example, you know, to think about how do you do that so you don't have to go by hand with something, or you know, so great sensor technologies, but they're all wired, right?

Which requires, you know, a whole set of infrastructure.

And so I think thinking that, you know, taking advantage of, there are lots of tools that have been developed in the IT space, for example, that in various technologies that will allow us to think about sensors that you don't need power for them, or you don't need to have wires that run to them to get there, or that they could be instead of, you know, Neda said instead of $3,000, it's $500, but what if it was a cent, right?

So I think that you'll see that continual driving to be able to measure different targets of interest and to do it in a way that's economical.

I think those are some of the things that I think are exciting on the, on the physical hardware side, and I think that the data side goes so fast, I think it'll be amazing to see where we are.

I mean I think the whole industry and what's being developed around AI and machine learning is going so fast that it will enable, you know, a lot of other tools and tools that, you know, you can also, we can, even if they're not developed for AI, may be capitalized on for, sorry, even if developed for ag could be capitalized on for the agricultural industry.

So I think it's gonna go fast.

- Thank you for those answers, I appreciate that.

Just taking one step back real quick.

We have a question in the chat about, "I'm in a very rural area, no cell signal.

"My internet is at my house.

"How do I find WiFi extenders to reach most "if not all of my farm?

"I raise goats and sheep." Anybody wanna take a shot at that one?

- I could provide what we do at Microclimate, so we have partnered with a company called Via.

So if you have internet at your home, we partnered with this Edge computing device that sits, our application sits on top of that.

You would go ahead and connect the Via into your router.

It provides the connectivity that you need.

It also provides a LoRaWAN, these wireless sensor communications.

So then you can also mesh the Vias.

So if you have a power source at, let's say, you know, a few miles, maybe by a grain bin, then you can go ahead and put another Via up there and connect the two as long as the signal is strong enough.

And if that's not the case, then you can always tap into Starlink.

So we have customers currently today that are in that situation, and they don't have any internet, so they use Starlink, and it works perfectly fine.

All the monitoring, all the automation is running perfectly well, and it's all happening on Edge, meaning on-premise, so the latency, there is no latency because it doesn't have to go to the cloud and come back.

And if the cloud is ever interrupted, your automation is going to continue running.

Therefore, if you're told a pump to turn on, the pump is going to turn on and turn off when it's supposed to, versus if the cloud is interrupted, it will lose its connectivity, will lose its mode of communication.

So there are ways around it, and it's going to continue to even get better and better because, back to what Cherie said with AI and Edge computing, I think those two are going to really start marrying one another.

We're gonna see more edge computing and AI.

That answers your first question.

My second part was really about where the technology is going as well.

- Anybody else have any thoughts on that one?

On the connectivity issue?

On that, I'm just gonna say that Pennsylvania currently has $1.16 billion available to help solve the broadband problem.

And we know from the work that we've done that 96% of all the crops grown in the United States are within 10 miles of high-speed internet, meaning precision ag is a solvable problem for farms all across the country, and there's money available in all the states to invest in high-speed internet.

So, but now it is happening right now, as we speak.

So, and will be, those funds will be awarded in each state over the coming months.

"As a small livestock farmer, where would I start?" That's a question that's in the chat.

So any suggestions on where to start?

Maybe I'll throw this one to Daniel and William and put them on the hot seat because they're kind of early adopters and tech innovators.

So if you had a small livestock operation or a small farm of any kind, how would you recommend people start?

- I guess I would start by doing what you're doing today and listening to this webinar would be a good start.

So listening to all the folks on here, and I would say talking to other folks, it doesn't have to be Daniel or me, I mean other folks that are, that you see in your area that are adopting something like that, and ask them, "Hey, how did you get your start?

"Hey, where did you start?" You know, and ask others that know more about it than probably you do.

So maybe just reach out to someone that knows a lot about that, and then that's where you get your start.

Then the snowball starts picking up steam.

- I think it's also identifying areas of greatest concern.

You know, where do you think your biggest challenges are?

Where do you think you can do the most improvement?

Once you kind of identify those, then you're able to search out solutions for those.

Again, is it crops, is it feed, is it rate of gain?

You know, all those areas are really different methods, so identify those most important areas for your operation.

- And I would just add to that, make sure the technology works for you and you're not working for the technology.

I know that seems like maybe a silly comparison, but you don't want to be doing tons of data entry when you're trying to automate something.

You want the technology to work for you.

And just as both William and Daniel talked about, talking to others and hearing what their experiences are with the technology to make sure it works in real-world applications in a farm, where there's dust and dirt and machinery, and maybe hired hands that might not be the best machinery operators or, you know, things happen, right, on farms in the real world.

And I come from a farm, so I can speak with some confidence in that area and know that things break on occasion.

So, anyway, make sure the technology works for you and you're not working for the technology.

Any other comments on that one before I move on?

We have time for a couple more questions here.

This one actually targets William, so I'm just gonna go straight to it.

Can you talk about the current state of drone technologies used for seeding and cover crops?

And I think Cherie, you have some work going on in that area as well at your NSF center.

- So I'm gonna assume that one might mean interseeding cover crops into row crops.

If it does involve that, I'm gonna say there, I know that there's some trials going on with that, and I've seen some data that goes both ways on whether that is feasible or not.

If we have a year like we had here in Pennsylvania, where it was really dry for a long stretch, it can be an issue because, you know, everything's canopied over and doesn't get much sunlight or rain, which between those two, you need a lot of both of those.

So, but there's a lot of data trials going on for that specifically.

And, like I said, I've seen good and bad on both.

So, I would...

I would probably try experimenting with that, whether it's in corn or soybeans or however you're using it.

But I'd say that there isn't a for-sure "yes" that it works or a for-sure "no" that it doesn't work.

So that's kind of what I've seen, especially here in Pennsylvania.

There's a lot of guys trying it, see if it works.

There's years where it works really well, there's years and it works not so well.

So, it's a trial by error, it's a lot of that so far.

- Yeah, Jim, you asked, I mean, I'll say, I'm not sure I can say so much in terms of the, you know, specifically, I mean, William has the personal experience in working with it for, you know, for say cover crops.

So I say one of the things that I think is, you know, maybe right now a lot of drones are actually somewhat larger platforms.

We use it to create very large data sets and, you know, and image large fields.

There's lots of ways to use it, for example, to spray technology.

But one of the things that I think is interesting is whether there are some opportunities not just to look from above the canopy down, but to get in and below the canopy.

And so one of the things that we are working on are even some smaller platforms that can navigate autonomously quite quickly and would allow you to collect data from below the canopy.

And then even not just, you know, often when we say drones, we think about aerial drones, but there are also some, you know, potentially nice implementations for autonomous-based ground robots that could, for example, you know, fit and navigate down a row.

And so that's, I think there are, you know, there are, you know, different, I think different types.

The use of drone technology, or the potential for it, I think, will expand as some of those capabilities for, you know, other size platforms, not just having one but using many of them at a time, right, that are coordinated with each other could offer, you know, a change at least in the future.

I think William's answer for today is better than the one that I'll provide.

- And I'm gonna wrap up.

We have a question from, it appears a student, but for students, what would you say is or are an area to focus on to get into this industry?

So, we'll end with the next generation thought here.

So, anybody wanna add to the conversation about recommendations for students and how they get into the industry, or the sector of the industry?

- Maybe Long and I can start because we're the one in universities and we'll let everybody else.

So, you know, I'd said, my feeling is, there's so much to do that brings, for a student, that brings in different disciplines.

One of the most exciting parts is that you get to learn from others who focus on a different discipline.

But, you know, depending upon what you are studying, think about how you connect to the problem.

You know, what makes you the most excited, that's what you'll do the most of, and that, you know, and so I would, my suggestion is to think about, you know, if you study, you know, even if within one discipline, what is it that you want to study and then, you know, go and find and work with others.

But, you know, there are lots of ways to meet students who work on different aspects, 'cause it's gonna take, you know, a tremendous number of different expert, you know, people with different expertise.

If I look at our center, you know, we have agronomists, plant pathologists, horticulturists, right?

Then we have electrical engineers, mechanical engineers, chemical engineers, all sorts of people who are work, coming together to work with different expertise and all of it's needed.

So I'd start with where, you know, what you're most interested in studying and then find connections and there are lots of ways that it'll take people working across those connections to make, to really make, you know, to to sort of build what we all envision as the future for smart farming.

And so, and build those connections.

And there are lots of, you know, companies, for example, in industry as well as in government who are very interested in this space, where, you know, you could certainly have a career that takes advantage of the expertise that you have.

Or you could start a company, that's another one that I think actually, and Neda can speak to that, but I think is one of the places that some of the newer technologies will find its way out first.

- Long, did you wanna add anything since it's a student-based question?

- Sure, I just thinking like, like I think we should start early, like for, if we want to have younger generation come into this industry, we should have them to have the technology be available for them to access at the very early time.

Like, for example, now we can look into K to 12 education and the students have the opportunity to observe, to actually hands on some of those things are very important and if for some students actually have idea about what's going on and then something they can contribute in the future.

So those are naturally transition from their mindset to in the future they're looking into this industry more.

So that's, I'm thinking, I just added to Dr. Kagan's point there.

- And I would just add that, you know, one of the most inspiring things I've seen recently around this whole space of agrotechnology and IoT advancements is our Nittany AI group hosted an event for agriculture and the environment and sustainability, was the largest event that they held, and it was about not being, not everybody was a computer engineer, not everybody was in agriculture.

It was about this transdisciplinary approach and coming together to make a better world for tomorrow.

The passion in the room was just phenomenal.

So, it was really a great indicator for the future of agriculture and the environment, in my opinion.

As time is winding down here, I think we got through most of the questions, if not all.

I wanna thank, once again, our panelists.

So thank you Neda, Cherie, Long, William, and Daniel for all of your great information.

We really appreciate everybody coming together for this particular webinar, and I would be remiss if I did not thank Dana for being the glue that holds us all together and makes all great things happen.

So Dana was, you probably saw some of her marketing out there, so Dana's a guru of all good things.

So, again, thank you everybody for joining us today and be look on the lookout for future webinars and you should get an invitation.

So, thank you very much.

Have a great day everybody.