>> Hey, welcome back everybody, Jeff Frick here with the Cube. We're in Denver, Colorado at the DJI Airworks Show. It's about 600 people, talking about commercial drones. Nothing for fun, no film, no movies. This is all commercial, agriculture, construction, public safety and really excited to have someone on the agricultural side, it's Greg Emerick. He's the co-founder, EVP of Bus Dev for Sentera. Greg, great to see you. >> Yeah, thanks for letting me be part of this. >> Absolutely, so it's pretty interesting. I think a brilliant move by DJI to break the drone up into the platform, the payload, and the software which we kept hearing about over and over in the keynote and even the software, they break into the mobile software to control the thing, and then the actual software on the drone itself to collect the data. And you guys have a really interesting product. You have a different payload option, then what comes out of the box and what most people think of. So tell us a little bit about what Sentera's all about and how you use this capability to build your business. >> Sure, we're based in Minneapolis, Minnesota. We have a team of engineers, a long history in remote sensing. And so what we've done is, we've taken these sensors that we build, there are a multitude of different types of the ones that we build, and we integrate them onto different DJI platforms. We've engaged their software developer's kit and so we fly the aircraft with the mobile app. When the airplane lands, we pull the data out, pull it into our software, do the analytics on it and then push it into other analytics tools that are used for agricultural purposes. >> So what kind of sensors do you use? What are the sensors? >> Some of them are as simple as a red, green, blue camera, just a simple image. >> Alright. >> Other times, it's multi-spectral imagery. It could be near infrared or red edge that would be used for crop stress or maybe the opportunity to apply nitrogen in some instances We also do machine learning, and so we'll do things where we're counting plants, identifying the location of weeds in the field, and then pushing that into other tools or other implements that then could go do something and apply maybe something to the field, or do something to help improve the crop production. >> So how was that done before there were these efficient, small, and easy to operate drones? >> Manually, and then, there was a lot of interpolation, where someone would go out instead of we can count every plant in the crop and in some instances, that can be done if you have a really small crop. Otherwise, they're just sampling and it's not very accurate and there's a lot of interpolation between maybe six different locations in the field on 160 acres versus counting all of them. >> Right, it's pretty interesting because we've seen that time and time again in technology space. Now with big data, we have the capability to not sample anymore, but to actually take all the raw input and take action on the raw input. >> That's right. >> It's got to be way more productive than a sample. >> Right, and what we're doing is actually facilitating a lot of tools that are out there today. They already know how much fertilizer they've applied. They know what the soils are like. They know how much precipitation they've had. But they don't know the status of the crop. And so what they need is sort of this real-time opportunity to look at it, understand what might be a problem or maybe there's no problem at all, but in the end, they identify what they might want to do and then, from there, create an application or a prescription to go out and do something. >> Okay, and then, from your business model, do they buy a drone outfitted with your sensors, do they buy the sensors, do you provide this as a service, how does the business model work? >> Yeah, so for us, it's all about the sensor and the software and so a customer will come to us, and they'll typically buy a solution. We've taken the time and invested the energy to make sure that they function and operate on all of the DJI equipment. And then, from there, they'll purchase a solution and that solution will then be used for their own business applications. It might be at the enterprise level, or it might just be a big grower. And from there, they'll take the data and then push it into their analytics tools. >> And push it into whatever analytics tools they have. >> That's right. >> That's awesome. So as you turn the calendar to 2018, hard to believe we're through 2017, I'm still, kind of can't believe that. What are some of your priorities for 2018? Where are you guys going next? How do you see this evolving, as both the market matures for acceptance as well as the technology and some of the cool things they announced here at DJI today? >> Sure, well you know, of course DJI does great things and we continue to work with them. You'll see new sensors come from Sentera. You'll see tighter integration into the platforms themselves and you'll also see a lot more data that comes from them, and how they'll be able to be used in other analytics tools. >> Excellent, alright Greg, well, congrats to you and your business, it's fun. We hear about the ag application all the time. I stumbled upon on YouTube, a dedicated channel just for drones for rice farming, which I thought was fascinating. (laughter) Like, wow, I didn't know that was a thing. >> Sure. >> But clearly huge impact in agriculture. Huge, huge benefit to food production, farmers and ultimately more food for all of us. >> That's the motivator for us. I mean, that's what get us up everyday, so it's great to have someone understand what we're trying to do, so thank you. >> Oh, a pleasure, alright, he's Greg, I'm Jeff, you're watching the Cube, from DJI Airworks, 2017. Thanks for watching.