S1E5: 2024 New & Emerging Technologies in Agriculture
Today we hear a presentation Mallory Douglass and Ben Fogle from EATS and CU-CAT at the South Carolina Ag Council meeting in February. They discuss opportunities for industry partnerships and some of the newest technology projects our student workers have been involved in.
Date: Feb 28, 2024
Ben Fogle – bfolge@clemson.edu
Kendall Kirk – kirk2@clemson.edu
Calculators & Web Apps
Feed Ration Calculator
Feed Ration Optimizer
Lime Rate Calculator
NPK Recommendations Calculator
2024 South Carolina Agriculture
Technology and Business Forum
Production Credits:
Introduction: Hannah Mikell
Producer: Kevin Royal
Editor: Kayla Peters
Technical: Trey McAlhany
Music Composer: R.M Davis
Special Thanks:
Transcript:
[Hannah Mikell]
Welcome to today's episode of Cultivate Ag. I'm your host, Hannah Mikell, Agronomy Agent for Clemson Extension here in the beautiful state of South Carolina. It's a pleasure to hear from the Manager of CU-CAT, Ms.
Mallory Douglass, as she discusses Clemson University's Center of Agriculture Technology, as well as Clemson EATS, short for Ecosystem of Agricultural Technology Sharing. I'm excited to learn how these two centers are poised to shape our ag technology. Next, we'll hear from our Lead Ag Engineering Specialist, Mr. Ben Fogle. Ben will enlighten us on some of the newest technology our student workers have undertaken this year. It makes me proud to know that ag technology is moving in such a positive direction with these two at the wheel.
Stay tuned for some valuable insights that could quite possibly revolutionize your approach to ag technology in the future.
[Mallory & Ben]
I'm Mallory Douglass. I'm the Business Manager with Clemson University's Center for Ag Technology.
So that's what CU-CAT stands for. So, if you hear us referring to CU-CAT, it's our Center for Ag Tech. We have a lot of acronyms and I'll break those down in a little bit, but we're super lucky that Ben Fogle is here.
He's our Lead Ag Specialist and so he'll get into more details on the technology and some of the solutions that are available for farmers now and that are also kind of in the pipeline. So yeah, I'm going start by talking about CU-CAT, EATS, and Precision Agriculture and then I'll pass it over to Ben for the fun details. I wanted to include a little breakdown here because we use these terms and it can get a little confusing.
Everyone who knows Clemson knows we have like a lot of centers and acronyms and titles for things. CU-CAT is our umbrella organization that all of the Clemson Ag Tech programs fall under. So, CU-CAT is about two years old, established by the Board of Trustees and also funded by the state.
They really believed in our mission to provide value for farmers and model that extension services model to really spread the information and be a service for the farmers and for the state. We're also across the state. So, our headquarters is Edisto Research and Education Center in Blackville, but our team is at, as you all probably are aware, Clemson has locations across the state and our team is pretty similar.
So, I'm out in Charleston, Ben's in Blackville, we have some team members who are on campus and at other research and education centers. So, it's nice that we can kind of be everywhere so that someone's always an hour or two away from anyone in the state. So then in the middle we have EATS.
So, this can be a little bit confusing, but this is how industry can get involved in a community consortium. So this is a newer program. We're building it out now.
The goal is to bring a diverse set of companies and external organizations together into one community and then listen to their feedback on what they view as important trends in the ag tech industry and allow them to weigh in on research, or a request for proposal to seed grant a research program. So, it's getting a group together, getting consensus on what's important for Clemson researchers to tackle in the ag tech space, and then providing them the opportunity to have their voices heard. And we also have other like networking opportunities for them and events.
So, I'll talk a little bit more about that, but that's what that stands for. And then we have Clemson Precision Agriculture. So, this is a program that's been around actually longer than CU-CAT has been established, but this is a program dedicated to ag technology and we kind of use precision agriculture and ag technology a little bit interchangeably.
It's a lot of overlap. So this now has a home with CU-CAT so we can extend our resources towards precision ag and to faculty and staff in that program. Our mission with CU-CAT is to collaborate and to serve the state.
So, we know that as Clemson and as a college we can do a lot of things really well, but we also understand that to really make an impact in ag technology for the state and beyond that we need to work with industry, funding agencies, and kind of everyone involved to really meet our mission of providing that value for our farmers. This is our team. So, we have an administrative team, a technical team, and education and extension.
So, we try to model that three pillars of the land-grant mission of research, education, and outreach. So, everyone on our team has a really important role in that mission. So, we have people who kind of oversee the organization.
We've got folks like Ben and Trey who work specifically in IT. They're really supporting research being done. And then we also have extension specialists and folks who even work with students.
So, we're trying to kind of tackle this from all angles here. So, we have specific areas that we work in. So, we try to kind of bucket those efforts into these emphasis areas.
So, with some examples, we will only take on projects and initiatives that fall into these categories because these are the areas in which we have expertise. As you can see, it's pretty broad so there's a lot that we tackle. Anything from app development, data-driven and connected solutions, validating existing and commercially available technology, testing new technology and its feasibility, and also farm operation and machinery automation and optimization.
So, as you can see, we do a lot in a lot of different areas and so there's kind of something for everyone. And we can meet kind of farmers and meet their needs in a variety of ways. And every project we do, we're taking it on because we see a specific farmer outcome or a specific outcome for the state.
So, these are the kind of outcomes of those emphasis areas. So, we'll only take something on if we see that there is a direct benefit to our shareholders and stakeholders. So, anything from environmental sustainability to increased profitability and productivity.
We've got, you know, helping folks manage their resources and assets and, you know, what projects can we take on to help with any of these resources. We also have outreach and extension to educate people on these technologies and also we are developing our own new technology. We work with industry and external organization in a variety of ways, are able to tailor our relationships to kind of have something for everyone because we really do realize that we need to work with a diverse set of companies and with different capabilities in order to meet our challenges.
So, we'll work with folks in sponsored research, we'll do grant co-applications, service agreements, so that's if there's an experiment that someone needs done, a more basic experiment, they can have that done at a station. We have student opportunities, capstones and creative inquiry, so we can get students involved which is always great for, you know, workforce development, networking for folks to know. Students have hands-on experience.
We have membership in that EATS community. We have field days, which I'm sure some of you y'all have probably been to field days. We have opportunities for people to sponsor those and then we also have the traditional gift and philanthropic aspect to our group.
So, these are our collaborators and supporters from 2022. These are kind of the first folks that were working with us and kind of on that past slide, they were working with us in a variety of ways, kind of anything from sponsorship of a project to sponsoring a workshop, being there at field day, helping us with app development, all kinds of things. So these are great, you know, groups that we've worked with in a variety of ways and then if you hit, this is the increase in companies that we work with now, so that's all the folks that we had engagements with last year.
So, as you can see, we've a variety of companies and a variety of capabilities and it's, it really takes coming across everyone at different angles to tackle what we're trying to do here. Joining EATS, this is what I mentioned, that consortium, we're kind of enrolling people right now and we'll continue enrolling people, anyone who wants to join in in our mission. We have opportunities for folks to join and they'll get access to student engagement, priority voting on research requests for proposals, the networking opportunity, there's always a marketing opportunity for members, and also anything that goes beyond providing actually cost of these benefits is philanthropic, tax-deductible.
If you're interested in learning more about that, contact me. There's a lot more details but just want to give a quick rundown of what that was and when we say EATS, that's what it is, it's a community. I'll now pass it to Ben to really talk about the fun stuff.
My name is Ben Fogle. I've worked at the Edisto Research Station since I graduated my undergrad in 2016. I've been a field technician under Dr.
Kirk since then and then in the Precision Ag Department, like Mallory said, that was there before CU-CAT was and then now CU-CAT exists so I moved into CU-CAT as the technician for it. We'd like to talk to you a little bit about some of the ag technology research that we do at the station and with CU-CAT and then also, some of the support that we give to student groups and how we can work, how we've worked with some of those industry partners that Mallory went over earlier. So, without our students, research and the station and the University, we would have no job, really.
We're very focused on student learning. We have the field days, like Mallory had mentioned, and we have student groups come down from campus and we show them what we do, what research we do, what work we're working on for the future. And one of those ways, like Mallory had mentioned earlier that we have some people on campus, we have Dr.
Aaron Turner and Michael Masters on campus. And Aaron is in charge of student engagement. We figured having somebody on campus that's actively involved with students should be the student engagement coordinator and then Michael, he's the technician down in the shop and he makes all their projects happen.
Without Michael, student capstone and creative inquiry projects would not exist at all. And where CU-CAT kind of comes in because those capstones and creative inquiries existed before CU-CAT came around. When I was an undergrad, I was in the Ag Mech program on campus and I did a creative inquiry as a sophomore and junior and then capstone project was, that's the senior design project.
Being able to assist them from the research station and bring in industry partners into some of these projects is really beneficial from a student standpoint, from a learning standpoint and from a networking standpoint because our hopes with bringing industry into these projects, and both of these pictures up here show projects where industry has been involved in the projects for the students. Our hope is that maybe the connections that these students make while they're on campus doing the projects for these industry people, that could potentially lead to a future job down the road or an internship for a creative inquiry, you know, a student involved in creative inquiry. This project with RM King, this is an example, we have two examples of how we have worked with industry on collaborative projects.
This project was with RM King, they're the only cotton picker parts manufacturer in the United States that doesn't outsource things to other countries. They're based out of California and basically, we wanted to see what the threshold was on when you needed to replace cotton picker spindles. So, they sent us cotton picker heads that came out from out in Arizona.
One of them had 1,800 picker acres on it. It was off a six-row cotton picker out there. They gave us another head that had brand new RM King spindles and brand new John Deere spindles and then one that was completely overhauled with new spindles, new doffers, new watering pads, new everything.
And we didn't really know what to expect when we went into this project, but we found that just based off of last year's data that we found that about 700 picker acres, so that's across all six rows, so it's 700 total acres on that picker is when you should probably start looking at replacing your spindle. That's a lot less acreage than what we initially thought and it was a lot less than what RM King thought, but it's really good news for them because you could use that as a marketing tool. We're working on this project for future works with another company for a new cotton picker spindle coating.
Chrome eventually is going to be outlawed in the United States like it is in Europe now. This was a steppingstone to work towards that. The project that we do with industry is with Michelin and we're working on some of their UltraFlex technology and their tires looking at different tire pressures, soil compaction.
This is a three-year study and we thought that we weren't going to see anything this year. We did it on peanuts and on cotton. The results, it didn't really show as much of a yield benefit for peanuts, but for cotton as you can tell it's not a significant difference, but it's a trend.
You can tell that a lower PSI would make lower compaction. It yielded more cotton per acre which was extremely exciting to us because we really didn't think we were going to be able to see any data from this year. Like I said, it's a three-year project.
They've done this out in the Midwest, corn and soybeans. They don't grow cotton and peanuts out there, so they wanted to see how it would affect cotton and peanuts. Up top is a footprint from a high pressure and then the one below that is the low pressure and you'll see makes that footprint a little bit bigger to decrease compaction.
Another way, this is going to kind of segue into what we do research-wise at the station, but we partnered with R&W Equipment. They're based out of North Carolina and they work on small plot research machines. While we were Precision Ag, we had already developed a cotton plot weighing system and peanut weighing system and grading system, also, that we currently use at the station.
In this picture over here, that shows our cotton plot weighing system. Instead of having to be in the basket and capture whatever comes out of the ductwork into a sack and then weigh it, it goes into those containment chambers, it weighs them and then dumps them out and then he can go and harvest the rest of the plot. Normally that would take three or four people to do that job.
Now it just takes one person. A lot of what we do and what we did in the Precision Ag Department and in CU-CAT, is to help increase productivity and efficiency for research and for growers in South Carolina. The next one, so we did the cotton plot weighing system.
We had the peanut weighing system along with the grading system and then R&W approached us about wanting to do one for Tidewater Research Station in Suffolk, Virginia with Virginia Tech and that's how we were able to partner with them. We just did the control systems. They did the fabrication of the ductwork and everything like that.
So, we have various different graduate students in our department and then ones that we also help with their research. Alex Samenko is one of our graduate students. He's a PhD student in the Engineering Department.
For his master's research and for his PhD research, he's looking at different peanut digger blade wear and different effects of fungicides on leaf spot and how that affected yield. For his PhD work, he was looking at blade wear and what we ended up doing was we borrowed somebody's six-row digger that right down the road and put blades on it and then just ran it through the dirt. And I think it was like a three or four day period of nothing but just sitting in the tractor digging dirt going back and forth, putting various levels of wear on these blades.
And we knew that it's not exactly the same amount of wear that you would see if you were to dig the peanuts but that was the best. This is what we had. So, we went from zero acres all the way up to 240 acres and what he found was that for every, say you harvested a hundred acres, that could potentially lead to a hundred and eighty pounds per acre of yield loss of peanuts.
That project is still ongoing. This was the first year of that. The next project that we've helped with is in the Entomology Department at Edisto REC with Adam Whitfield, he's a PhD student, and Dr.
Jeremy Greene who's the Entomologist at the station. They wanted to simulate different levels of defoliation for insect damage to quantify how much yield loss was associated with this much defoliation versus that much defoliation. So, we developed a hole puncher, basically, that's pneumatically driven and we at first built it out of aluminum thinking that would be better.
But we had a fellow working with us, Curtis Erwin, that was really big into 3D printing and he was like, well let's try to 3D print it maybe and make it a little bit more modular. So that's what we ended up doing. He trained the PhD student how to run a 3D printer.
I still really don't know how to run one. But he made this, Curtis, made this project possible. The next slide shows what the leaves look like.
I'm really glad that I was, I only had to be involved with the designing and help building the things because they had a group of students that just, all they did was they sat on a five-gallon bucket and punched holes in all these leaves. So, this is an example of 15% and 30%. If you went above 30% you were ripping the leaves up and you couldn't get the leaves out of the dies that pressed the holes in them.
For the leaf punch they did, you know, a zero - which they didn't do anything to them. The 15 to 30 and then 100% defoliation which I think they just chopped off trifoliates. They also did stem damage so where I think they just went out there and just cut at different rate for growth rates throughout the year.
The next project that we've helped with is with Dr. Cory Heaton and a PhD student, Perry Loftis, who's looked at deer damage. And I know being a farmer myself, I understand the importance of not wanting deer to eat up all your crop.
At first, we did this project with the Soybean Board and you can tell in that picture right there it's kind of our pride and joy picture, not for the farmer but for this project. They (deer) literally went up to the cage. There's a little exclusion cage that we built right here - and we would have them strategically put or just placed randomly throughout the field and they (deer) literally just came right up to the cage and ate the soybean.
And then this image here. A deer population survey software that our department also developed to help quantify how many deer were in a specific area. We've also done this project with cotton and in peanuts and that picture is literally the same for cotton and for peanuts.
And you wouldn't ever imagine, I never would have imagined when I was going out in the field and collecting data for this project a couple years ago. It was the first year that we did it. Normally, you can look out over a peanut field and it all is flat, it's all uniform.
You know deer - doesn't look like a deer hit it more here than this area, but in those exclusion cages you could tell it was a completely different, completely different foliage. So now I'm going to go into some of the research that we do at the station. These are pictures of peanuts.
This is peanuts here and this is basically we've developed a software that can remove the background and look at canopy coverage, it can look at canopy coverage rates and look at leaf tissue nitrogen rates, and we use this program now really to look at nitrogen deficiencies from poor nodulation. We worked with USC Aiken on some of this with different types of soybean varieties that are out there. Some that may not inoculate as well as others, just to try to find a certain trait that that they're trying to find for inoculation.
Like I said we use this for leaf tissue nitrogen. We went out there, we actually grabbed trifoliates off the plant, sent them off to get them analyzed, and then used that to train the software that we developed - train the models to predict the leaf tissue nitrogen. It turns it into an RGB value.
You can tell over there it's a lot more yellow than this side. It's just an image so what we normally do - so this is a real-life application you know this is research but it's a farmer's field. A fella called us and said my field, there's a lot of yellow spots in this field and I'm thinking about putting out some urea to help out because I don't think it's nodulating as well as in other parts of the field.
So, what we did was we split this field up into 250 foot grids and each one of those spots I flew the drone over, I took a picture, it was a top-down picture. This is where we converted those images into pixels and the color picture of where the yellowing in the field is. So, the red spots right here, this is a contour map based off of those color pixels.
The red spots were poor nodulation or weak nitrogen levels that we have predicted in our models and then the green is like a higher nitrogen level in the leaves. And it's basically using kind of like a, I don't know if you've heard of NDVI or not, but that's essentially what we're able to see. It's not a true NDVI but it's an estimated NDVI based off an RGB picture.
So, this is the image. This was the contour map versus the actual oblique image that I took with the drone. Where it's red here is where it's yellow here, and the red is correlated to like low nitrogen levels in the, in the leaves.
So, we made a prescription based off of the contour map that we created in the software that we developed. And we uploaded that to the spreader truck when they went out and spread urea. And it was basically an on-off - so this area got urea and this area didn't.
This is soybeans. We thought it had to do with poor soils and poor inoculation. Well, there used to be a road right here.
It used to be a road right there. And that's what the farmer that we were working with he said there used, it never produced soybeans in that. We went through the field, spread urea in the spots where we thought we needed to spread it, and this was before - and this is after.
And then there's another picture. This is the back part of the field before and after, so it was, it really helped and the farm was really impressed with it too. In some years it doesn't pay off to do that, but.
So, you can soil test, you can split it up into different zones or different grids, however you want to do your soil test analysis, but this is like a rescue-type thing. This is after you've already spread all your nutrients at the beginning of the year before the soybeans were even planted. Soybeans come up and you're like man something's wrong with this field and this is just to help push it to actually produce something.
This was just a farmer called us and said my field looks terrible, is there anything you can do. And this is what we were able to do for him. And I forget how much he cut off this field, but it was significantly more than what he had done in years past just by not putting anything out.
And it also saved him a lot of money by not broadcasting the whole entire field with urea, either. And he could have gone out and done soil test while the crop was there but like I was saying earlier normally you would spread all your fertilizer at the beginning before you plant. And in soybeans you normally don't put urea out to it throughout the year anyway.
It was just nitrogen. So now I'm going to move on to some of the digital solutions that we're working on and some of the future things that kind of looking at. So, CPIT, I don't know if many of you people have heard of CPIT or not, but that's our Center Pivot Irrigation Testing program that our department kind of created and its kind of morphed into the water agentsí duties now.
This is Becky Davis. She's the head of the CPIT program. She's one of the water agents across the state.
Probably collectively we've done several hundred tests, three or four hundred tests, across the state. I think we did about 200 of them before we even released this as a publicly available thing where we would go out and do a test for somebody to make sure it was right. As of 2020, that was when we kind of started doing these tests.
It was a good excuse for us to social distance at you know, during COVID. But we went out in the field and we figured out whether or not somebody's pivot was uniform or not, and what - how efficient it was or how [in]efficient it was. And we were really shocked to find out that even some of the brand new systems were just as bad as the ones that were 20 years old if they weren't nozzled correctly.
And there was one that we had that a fellow had just put up three or four months before, and we checked it and it was worse than some of them that were 20 years old because they had nozzled it incorrectly. And you wouldn't know that - the farmer didn't know that, and he wouldn't have ever known that except for whenever the crop is growing and there were waves in where the rings were when it went around in the field. The only other program like this is in Florida that we know of.
They go out there and they basically take a tape measure the whole distance of the pivot and they drop buckets every 20 feet and then they come back they collect them. It's essentially the same thing we do except for we don't have a tape measure. They go out there and then run the pivot over it and then pick all the big buckets up and measure them and then they have to spend a couple of days after that to do hand calculation.
All of its hand calculation. Where we're different from them is that all this is computer-based, it's a GPS based, so each one of these little balloons right here is a cup, a catch cup, and they're 10 feet apart, and I mean they're like little solo cups that we put out there. And we stake them down to the ground so they don't fly away.
And then we run the pivot over it and we collect them all, measure all the cups and mark any geo, we call them geo notes, but anything like a leaky boot or a clogged nozzle or a hung drop. We can hit generate report and they have a report right in the field. And this is an example of one of those reports - a uniformity report.
And this one's actually not a really, not a bad one. If you take from here out, because that's your end gun and a lot of, in a lot of cases people don't even use their end guns because they're so inefficient. But this one, normally if you go below 75% uniformity that's whenever you're looking at needing to re-nozzle.
This one was at 87 to the last hour and then there was only one geo note there at the beginning and you can tell, you know, there was nothing there because it didn't collect anything. And then this also is generated in a report. It's a new percent timer chart.
A lot of the pivots they'll come with a percent timer chart where how you would you would be able to set the speed of the, of the system, because that's what dictates how much water is getting put out. It's just the time it takes to go around in the circle. And a lot of them are wrong.
And a lot of that's due to just age on the equipment, on the nozzlesí orifice sizes. They get bigger with time and some water is harder than others. And so there was a an example of that.
There was a fella in St. Matthew's that we did a test for, and he thought he had been putting out an inch the whole time. He said I know that it's not putting out enough but I'm doing what the chart told me.
And he was actually putting out half of what he thought. He was only putting out a half inch. He was - he knew something was wrong but he didn't know it was actually wrong until we did these tests.
That had to do with, I think it was because it would, it just wasn't running fast enough. This, this picture down here is the application chart and these - this can help you with determining where to put soil moisture sensors. So, you normally wouldn't put one in the more green area because that's an over-application of what the average amount would be - that you want to put out.
And then the red area is an under application. Your yellow areas are kind of your average and that's where you normally would want to put a soil moisture sensor. And then the image up here shows where the end gun should, should cut off according to the field boundary so you don't get water blasting into, onto a road and not being able to see whenever you drive down the road because the water is hitting the windshield.
I know that's a big problem people pointed out. Now I'm going to move on to some image analysis that we're working on. This was not a funded project this was just something that we were like, oh, this might be cool to kind of look at.
All right we've put in proposals with the cotton and peanut boards on this for this year, but this is yield estimation based off of taking your phone, and taking a picture of a row of cotton, or a windrow of peanuts, and to estimate how much yield you might have. So, the next slide is kind of a quiz. Which one of these do you think has a higher yield?
The right or the left? Because when we harvested this, when we harvested this that was how much we were able to harvest off of that side and then off of this side here. And that, those are raw actual numbers from when we harvested the plots.
This side was the less yield but we took that picture, we took a picture of it, and we ran our algorithm over it and the algorithm was made based off of estimating cotton yield loss using a drone. We thought maybe we could apply it to just taking a picture with your phone and seeing how much yield was out there. And it was only 0.
6% off from the actual total value so it was only seven pounds per acre difference. That one was really good but the next slide will show that there's some that's a little bit more - the algorithms not quite there yet. Like I said it's the first year we've worked on it so that was 11 and a half percent there off, but it did say it was less instead of it actually being more.
There's another quiz for you. Which one of these peanut windrows do you think has the higher yield? Top one?
And you're right. The yield for that was 5,700 pounds, almost 5,800 pounds. Our model predicted it being 5,200 pounds, so 500 pounds off - 570 pounds off, but it was only 10% error.
In the next slide that was a 2. 8% prediction error off. I just wanted to show you there's some good predictions and some bad predictions, and we're still kind of working it out.
So, on average with the cotton we had a 6% average of error, and then peanut was 7. 6% error. Another image analysis project that we've worked on with other grad students was with Hope Becton and the peanut specialist Dan Anco on just being able to take a picture, like a farmer could go out to the field take a picture of what he thought was injury on a peanut plant, and it could say whether it was paraquat injury, it could have been a healthy plant, or it could have late leaf spot.
And that would alleviate some of the questions that some of the researchers - if an extension agent couldn't get to him at the time and they just wanted to know what they needed to spray or maybe had some Liberty left in the tank and didn't clean it out all the way. This was the reason why it looks like that. Another software that we've been working on with image analysis is being able to predict peanut maturity.
Instead of having somebody go out there and separate it from orange, brown and black, that's how you determine peanut maturity - you pod blast them to get the exocarp off of the outside of the peanut pod, and that's where you get all these different colors from. You don't see those colors until you take that exocarp off. And then an extension agent, or somebody that is familiar with this process will separate orange, brown and black pods and then the percentage of that will be what the actual maturity of the peanut is, and whether or not it's ready to dig or not.
But my orange, brown and black will be different than her orange, brown and black. And, also, I didn't know I was colorblind until a couple years ago. Mine could be way off.
Using the software- based analysis takes the guessing game completely out of it and that's currently being developed, also. But we have thousands of peanut samples that we've taken from extension agents, we've done them at the station ourselves, from the peanut crew they - every sample that they run they take a picture of and then we get rid of the background. So, this was on a background.
It was like a blue background, I think, and we got rid of that background and just had the peanuts there. The amount of processing time and the amount of time it takes to go through a lot of those samples - it takes forever. So, we have a graduate student now, Virginia, that is working on how to speed that up.
Whether or not full resolution is better than maybe a not so great resolution of picture. And we found that the 250 kilo pixels is sufficient enough to be able to see whether it's a Orange, brown or black pod. Anything lower than that, it gets kind of pixelated and can't really tell.
But it has sped up the process 18 times so instead of taking 18 seconds to run a sample and now it takes one second to run the sample. The amount of time and the amount of money that it cost to process these things - it's significantly increased, so that's been a big game changer for us. So now I'm not going to go through all these web apps that we have.
I know there's a couple of handouts that we had up here. If you're interested in any of these you can go, you can take a picture of this with your phone, but these are some calculators and some web apps that our department has created to help growers increase their knowledge on kind of what to do. There's like a peanut digger conveyor speed calculator.
A lot of people they would just kind of guess about how fast their conveyor speed is versus their ground speed. This takes the guessing completely out of it. It's basically a stopwatch and we've figured out how long it takes or how many bars are on a chain, and you can set your conveyor speed based off of that.
There's also a pivot fertigation calculator and a lime calculator. We've had a really big impact. 177 countries have seen these.
People from all over the world have come in contact with these calculators. This is a map of the US. Texas and Illinois have really been the ones - especially for the EMC calculator - for grain bin storage.
That's been a really big one out there. And then this is a map of South Carolina with the reach that we've had in South Carolina. We have a feed ration calculator that was brought on by Scott Sell.
He used to work at Edisto REC. That's to calculate a nutritional analysis. And this is just an example of the inputs that you can put into the calculator.
And then this is what your mix sheet is coming out on the back end. The feed ration optimizer - that can automate the process that we do with the feed ration calculator, but this is to optimize feed costs. And then you can take the output from this calculator and push it into the feed ration calculator to figure out what you want to feed your cows, or your sheep, or your goats.
You can do that, too. And then the lime rate calculator - that was one that we created to calculate the lime requirements based off of soil condition. So, you can just figure out what your soil pH and your buffer pH and then what your mixing depth is.
And then it will basically spit out a recommendation based off of those two values and then you can compare different sources of lime, also. The next one is the MPK calculator. So, say for instance I forgot to put on the soil form that I wanted to do the analysis for peanuts, but I had put on there that I was planning on doing cotton this year.
And then I changed my mind. I was going to do peanuts. I would get the analysis back but then I'd say well what do I need to do for peanuts?
So, I can go in to the calculator, put the same soil code in there, the crop name will be your crop code, and then put the levels of phosphorus and potassium in there that comes off of the soil report that you get, and then it'll tell you a recommendation for that specific crop. There's a bunch of different crops put in there. So, now I'm going to give it back over to Mallory to talk about some of the future stuff we're looking at and how we're moving forward.
Thank you, Ben. So, I know we're running up on time here but on these handouts, on the backside we have different QR codes for we have an event coming up. It's the South Carolina Agribusiness and Technology Forum, March 20th.
Please register if you're interested. It's free. We have panelists on four different topics.
We've got sustainability, ag tech, fruit and vegetable opportunities, and economics. It should be a really good networking opportunity and just a really great learning experience for everyone because we've got great panelists and a really diverse group of hosts and people who are coming. So definitely check that out.
We're co-hosting it with Palmetto Agribusiness Council, with SCDA, SC State, Mixon Seed, and Farm Bureau. It's a great group of people who are hosting it, so definitely check that out. We're going to be increasing our events.
We're going to be doing more across the state, and just really trying to be out there in the community sharing and giving networking opportunities so many of you can move forward. So, what we're trying to do here is we're trying to collaborate. We are trying to, you know, through education and development, help folks adopt new technologies to help them and be a resource for the state.
Follow along with us if you're interested in EATS and joining that is like a company member. Please reach out. I'd love to meet with you.
We have a mailing list QR code on there. That's my contact info and then that is our CU-CAT Director, Kendall Kirk's contact info. So, if you have any questions, like we're please reach out it could even be specific and even if you email me a specific ag tech question that I might not be able to answer, I can always point you in the right direction.
So, I'm here to be a resource for y'all, and we look forward to growing and onboarding more people and helping folks start implementing these technologies. So, thank you so much for having us, Steve. Thank you for inviting us we're really happy to be here to talk to y'all and, yeah, follow along.
We're pretty new, so CU-CAT ís only two years old but we hope to keep growing our team and hopefully you'll see a lot more of us.
[Hannah Mikell]
What a pleasure it is to hear from you both. Thank you, Ben and Mallory.
That's going to wrap us up for today's enlightening presentations. We've explored the many different areas of what Clemson has to offer. I'm looking forward to all the new technology our students are developing.
Remember staying informed and adaptive is key in our ever-evolving landscape of agriculture. Thank you for tuning in to Cultivate Ag podcast where we take you from the classroom to the cab. Stay tuned for our next episode.
Until next time keep it growing. Clemson Extension is always striving to better serve our growers as we hope that our podcast is simply another tool in the toolbox. I'm your host Hannah Mikell.
Thank you for joining us. Remember from our classroom to your cab we wish you a very successful growing season.