 So, as Mike said, my name is David Kramer. I'm fairly new to NDSU as a whole, this is my first year here. But I have been in the GIS side of things for about 25 years. I'm fairly certain that if I asked each one of you to divine what precision agriculture is, I would get, you know, different answers from everybody. It's pretty broad. So at the CREC, one of the things we're doing in terms of precision ag is using UAS to support a lot of the field trials. So you were out today, you were talking to people about fungicide or herbicide applications. And a lot of what we're doing with UAS is collecting imagery that we can then use to correlate against that. And so we can look at how the electromagnetic spectrum interacts with the leaf structure to give us information about the vegetative health, if you will, of the plants. That's just one aspect of precision ag. I mean, when we talk about precision agriculture, we have variable rate application technology, variable rate herbicide application. We have LIDAR data sets that we can use. But I think the one thing that holds true about all of it and what we're trying to work on here is how we manage the data that comes out of this. Without question, one of the biggest driving factors of precision agriculture is data. How many of you all have yield monitors? How many of you actively use the data from your yield monitors? There's a lot of neat information we can get from that. If you want to look at inner field variability in terms of profit or loss, you can use that yield data, couple it with market conditions, and come up with profit maps that show the rate of return per acre across your fields. That's just one small application. All of the GIS data that you use for variable rate application comes out of a GIS or a geographic information system. So what we're trying to do here is figure out how to use this technology to improve, for example, prescription maps. The higher the resolution data, the more accurate the prescription maps can be. If we use typical aerial imagery, we might be getting about a 1 meter ground sampling distance or a 1 foot ground sampling distance. When we start talking about UAS, we can get down to sub-centimeter ground sampling distance. In some cases, I've done applications where we're identifying individual blades of grass, individual species of weeds at sub-5 millimeter pixel resolution. But all of these types of data can feed into our prescription maps. And so one of the projects I'm working on with colleagues at NDSU is how do we do this? These data are massive. Some of the images we get are 3, 400 megs apiece. It's a lot of data to work with. It's a lot of crunching of numbers. Out of curiosity, how many of you have just a consumer grade drone that sits on a shelf that maybe you bought for ag but never used? Does anybody have like a Phantom 4 Pro, a P4P? There's quite a bit of information you can get without actually going out and specifically targeting something like the M600 that has a 10-band multispectral sensor on it. Even with the P4P, you can scout your crops. There's a number of vegetation indexes that utilize only the red, green, blue portion of the spectrum. You can get meaningful information out of that to look at crop health, chlorophyll content. The list goes on and on, frankly. Moving out of the UAS side of things, one of the focuses I've been working on here is that data management issue. Taking a lot of the spatial data we have, the field outlines, the trial outlines, integrating them into a geographic information system so that we can take our maps and put them online. That allows our workers to get out in the field without having to carry a three-ring binder or a big notebook and they can just open up their phone, click on a trial and pull up the information regarding that trial. What are the dimensions? What is the crop? How big is it? When was it planted and information like that? Suffice to say, as we collate these data into these data sets, these databases become pretty big, right? Managing that is going to happen better from a centralized server database or some type of application where we can do that. From an individual standpoint, you don't need that. If you get out and you fly your drone, you get images of your crop, there's any number of ways to process that without any overhead. Drone deploy has free trials. A lot of places will process smaller amounts of imagery for no cost. You can still get out and fly that thing and have fun with it because let's face it, flying drones is a lot of fun. We actually got out this morning and flew a field like 8.30 this morning before the field day started. But really in what we're doing here, the precision ag component of the CREC really supports pretty much all of the trials going on here. We really do. We're collecting imagery across the board on everything from hemp to canola to cereal grains to soybean, to corn and utilizing that information to make decisions regarding nutrient application or herbicide application or something like that. Moving forward, I expect we're going to see a more targeted effort towards things like weed mitigation and Rex who's going to go after me is going to talk a little bit about that. We have unmanned aerial sprayers now. The person talking after Rex is going to be talking and discussing about a robotic rock picker to go out and clear your fields because nobody likes going out in the fields and picking up rocks. I did that as a kid. It wasn't any fun. So the precision ag program here is evolving and it's coming along. It's moving a good direction I think in terms of how we deal with all this data. And more importantly, not how we deal with, but how we utilize it. For those of you that have your yield monitors, you've got a phenomenal amount of information at your fingertips that can give you quite a bit of information regarding how your fields are producing. What is your return? How are you utilizing it? You know, more importantly, we can take a lot of this data and start looking at things like above ground biomass. Start calculating out and understanding above ground carbon which feeds into a carbon market and carbon credits. So I want you to kind of think about that just in terms of your own fields and what you're doing because there's a lot of opportunity to use this type of data. Even if it's not directly towards precision ag, you can use this for things like carbon credits. You can use it to understand yield. You can use it to relate back to profits as I've mentioned.