Wayne Woldt - Unmanned Aircraft for Agriculture and Natural Resources
There is an increasing level of interest in using unmanned aircraf to help with understanding and managing agricultural production operations and natural resource systems. Tis seminar will include a review of unmanned aircraf research and development toward early detection of moisture stress in cropping systems, as well as other current research projects that are underway in the Nebraska Unmanned Aircraf Innovation, Research and Education (NU-AIRE) laboratory. Specifc areas of presentation emphasis will include: deployment and fight operations of unmanned aircraf systems (UAS), sensor system development, sensor integration, information management, review of fight rules, and fight safety
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[00:00:00.636]And I thank you for giving me the chance
[00:00:02.429]to visit today on an unmanned aircraft
[00:00:05.596]They're kind of a disruptive technology.
[00:00:09.980]I think most of us can think about some way
[00:00:12.442]that they've heard or seen some
[00:00:15.227]disruption generated by unmanned aircraft,
[00:00:19.188]and yet at the same time they can be
[00:00:21.641]an exciting opportunity and technology.
[00:00:25.669]So today I though I'd give more of an overview
[00:00:29.312]of a number of different projects that we're working on,
[00:00:33.707]this is kind of the first time I've done this,
[00:00:36.940]actually sat down and pulled em together
[00:00:39.602]and try to summarize different perspectives,
[00:00:43.713]different types of aircraft and so we'll see how it goes.
[00:00:51.094]This is the first PowerPoint I put together
[00:00:54.384]that went over a gigabyte, and that's because
[00:00:58.292]there's thousands of slides so I hope you're ready,
[00:01:02.293]and you got your toothpicks in your eyes.
[00:01:04.844]No there's video in here, so we'll see how that goes.
[00:01:10.220]So today I will give a little bit of background.
[00:01:12.766]I always try to set the context for unmanned aircraft, and
[00:01:17.150]then visit most of the time about these selected projects,
[00:01:21.668]talk a little bit about aviation safety,
[00:01:24.835]which is also one of the projects that we're working on,
[00:01:28.712]and then if time, we'll look a little bit
[00:01:31.974]at flight operations and how we go about setting up
[00:01:34.510]and planning flight, and then the idea of data to knowledge,
[00:01:37.616]because while it's really fun to fly these things.
[00:01:40.416]We're actually after knowledge.
[00:01:46.157]Well that's the cover story.
[00:01:48.872]So what's the real reason that I'm here?
[00:01:56.597]It's because I'd like to think about, have you think
[00:02:00.128]about new ideas and explore new ideas for unmanned aircraft
[00:02:05.309]and ways that this technology might be able to help
[00:02:08.553]you accomplish some of the things that you're trying to do.
[00:02:12.222]So that's really why I am here.
[00:02:13.960]That's kind of the background story.
[00:02:17.739]And also to continue to evolve a connection with CALMIT,
[00:02:22.712]we're kind of talking, and dialoguing, and CALMIT
[00:02:25.680]is renowned for remote sensing,
[00:02:30.451]so unmanned aircraft are another level of remote sensing,
[00:02:34.681]so it makes sense to connect up and find ways
[00:02:37.899]to join together and to continue to evolve that connection.
[00:02:44.552]Well, a little bit of background here,
[00:02:46.647]and I leave this slide in because this is a slide
[00:02:49.506]that I used to use, and you'll notice it says,
[00:02:54.240]that unmanned aircraft will be legal to fly,
[00:02:57.979]and I had to have that slide for I think about
[00:03:00.482]the first four years or so of work in unmanned aircraft
[00:03:05.119]because they weren't legal to fly,
[00:03:08.199]and so this was the slide I used,
[00:03:10.857]now I can cross out the will be
[00:03:12.522]and say they are legal to fly.
[00:03:16.233]We know agriculture is gonna be a natural forum,
[00:03:20.487]also awesome potential for natural resources.
[00:03:23.945]I'll have some applications on natural resources here.
[00:03:28.133]Ag is projected to be the kind
[00:03:30.877]of the lion's share of the market,
[00:03:32.695]about 70% of the unmanned aircraft system marketplace,
[00:03:37.750]as it evolves, and as it matures.
[00:03:41.853]The Federal Aviation Administration does regulate
[00:03:44.329]unmanned aircraft systems and the new rules
[00:03:49.047]that make it legal to fly these
[00:03:51.317]now were finalized in August of 2016.
[00:03:55.137]So not that terribly long ago.
[00:03:59.814]And we know that this aerial view
[00:04:02.461]is really a strategic advantage.
[00:04:07.109]So a little bit more on the background,
[00:04:09.988]and this is leading into one of the projects
[00:04:11.910]I'll be talking about, we have this concept
[00:04:14.557]called the yield gap, and this is the difference
[00:04:18.182]between our potential yield, if everything was really
[00:04:23.375]perfect and we could get everything that we want
[00:04:27.142]out of the crop to our actual yield here.
[00:04:33.271]This is a gap and this gap occurs because
[00:04:35.893]we have some limitations, we have water, nutrient,
[00:04:39.279]we have pests, things that are
[00:04:41.141]also affecting that crop production,
[00:04:45.622]and that gap then is an inefficiency,
[00:04:50.757]and so a question could come up, you know how about
[00:04:53.796]unmanned aircraft, can they help with that gap.
[00:04:57.645]Maybe in the case of water limitations, if there's
[00:04:59.922]a water limit, and we know there are limitations
[00:05:02.405]even in Nebraska, because we have allocations
[00:05:05.164]at different natural resource districts,
[00:05:08.229]and so producers need to think about how am I gonna
[00:05:11.100]make the most use of the available water.
[00:05:15.341]Then there's precision agriculture as another potential
[00:05:18.828]mechanism here to perhaps close that yield gap
[00:05:24.342]or help ameliorate it with available resources.
[00:05:29.300]And here the idea is to farm based on knowledge about
[00:05:35.430]field variability, spacial variability of other variables,
[00:05:40.485]weather for example maybe a factor, storms,
[00:05:45.202]thunderstorms can have a tremendous amount
[00:05:47.959]of spatial variability, and so you've got different
[00:05:50.453]water levels, precipitation levels,
[00:05:53.783]that may have hit a given field or area.
[00:05:57.789]So precision agriculture then is farming based
[00:06:01.341]on knowledge of that variability
[00:06:04.188]and making use of that knowledge.
[00:06:07.813]One particular type of precision agriculture that's
[00:06:10.990]evolving is variable rate irrigation.
[00:06:13.914]So this is an example of a center pivot
[00:06:16.676]and then it can apply now based on a zone rate.
[00:06:22.388]This is an interesting case where technology
[00:06:27.230]in terms of the irrigation may have actually
[00:06:29.709]surpassed our ability to manage it.
[00:06:33.420]There's also sector based center pivot,
[00:06:37.417]where the rate changes just as the pivot
[00:06:39.904]goes around but it changes down the whole line.
[00:06:45.940]So a question might be asked how can we set
[00:06:48.139]those zone irrigation rates on that complicated system,
[00:06:54.090]that center pivot system, it's a big system,
[00:06:56.527]those are big, there kind of like a freight train
[00:06:58.904]rolling once you get em started you can't just
[00:07:01.579]switch em on and expect em to irrigate the whole field
[00:07:04.380]in an hour, it takes a fair amount of time and effort.
[00:07:08.119]So, you wanna do things right.
[00:07:10.176]Well, you know you're looking at a field like this,
[00:07:12.474]this is the view you've got, guess how much you wanna
[00:07:16.718]put on, maybe check what the neighbors are doing,
[00:07:19.992]that's always a good source of information.
[00:07:23.494]Water budget, hey now we're getting a little
[00:07:26.042]more sophisticated, maybe like a checkbook,
[00:07:28.588]trying to keep track of what's going in
[00:07:30.395]and approximately what's going out, what the balance is,
[00:07:33.643]and maybe even going into soil sensors,
[00:07:36.219]and trying to collect some data.
[00:07:38.485]Traditional soil sensors though take a lot of soil sensors
[00:07:43.275]in a field, so you can tell where I'm heading with this one.
[00:07:49.025]We need a new view and here's an aerial view.
[00:07:52.444]Now things a little different right and you start
[00:07:55.929]to assimilate information in a different way.
[00:08:00.157]So unmanned aircraft systems come into play then.
[00:08:03.349]This is a Tempest, this is the first aircraft
[00:08:06.149]that we really worked with, about an 11 foot wingspan,
[00:08:10.087]10 to 15 pound payload, it's got all the features,
[00:08:15.755]flaps, allerons, rubber, elevator,
[00:08:19.018]nice, easy 40 mile per hour cruise.
[00:08:22.643]It'll go up to a 100 as Buddy says, you have
[00:08:25.947]a bit of a dive but it'll get there.
[00:08:29.389]Yeah, very nice aircraft, fully autonomous flight
[00:08:34.445]capability and we also fly the multi-rotors,
[00:08:40.546]as you'll see, so we're equal opportunity.
[00:08:46.354]But again we're not out there just to fly,
[00:08:49.702]although that's the fun part,
[00:08:51.677]we've got sensors, we gotta collect data.
[00:08:54.675]So here's a multi-spectral MicaSense, RedEdge,
[00:09:01.514]it's called sensor, a FLIR Tau 2 thermal infrared sensor.
[00:09:07.532]So you can buy these sensors and now you got sensors
[00:09:12.923]and you got an airplane, you don't have a mount
[00:09:16.996]to put the sensors in so that's where the students
[00:09:19.552]come into play, these brilliant students they know
[00:09:23.375]how to work with solid works and design,
[00:09:26.634]so they'll draw up a design and send it to the printer.
[00:09:30.991]The 3D printer, and the 3D printer prints out this device,
[00:09:35.034]this thing right here, which is our sensor mount.
[00:09:38.954]So now we've got a mount to mount
[00:09:40.690]the sensors in the airplane.
[00:09:43.561]So any sort of mention of product or trade.
[00:09:50.602]Not meant to endorse it.
[00:09:53.290]So here's a little bit of a closer look, here's
[00:09:55.377]the MicaSense, here's the Flir Tau 2 mounted
[00:09:59.746]on to the 3D printed, you can see there's wires around.
[00:10:03.020]We're kind of integrating this, we're putting parts
[00:10:05.275]and pieces together in the lab,
[00:10:07.332]cos you just can't go out and buy this stuff,
[00:10:09.516]at least when we starting out you couldn't.
[00:10:12.947]It's getting better now, manufacturers are starting
[00:10:15.411]to do more integration and you can buy em kind of set up
[00:10:19.555]although you'll notice here, we've got two sensors,
[00:10:22.917]we wanted to fly two sensors at once,
[00:10:26.835]and the sensors were selected to give about the same field
[00:10:30.068]of view, so every time they're triggered,
[00:10:32.644]we get the same view out of both of em.
[00:10:36.004]A lot of times now if buy an unmanned aircraft,
[00:10:40.301]you can fly one sensor at a time.
[00:10:43.493]So there are kind plug and play, I wanna unplug
[00:10:45.733]my thermal sensor and plug in my optical sensor
[00:10:49.362]and go fly, so now if you wanna fly and collect
[00:10:53.125]let's say, two or three sets of data, you gotta fly
[00:10:55.311]the same field two or three times, it takes more time.
[00:10:59.145]So we wanted to cut down on time in field,
[00:11:02.490]fly the field one time and collect our data.
[00:11:07.812]Side-view, there's the 3D mount, the sensors set up
[00:11:12.210]there, bottom-view, this is the business end here,
[00:11:16.694]the thermal and then the high-band, multi-spectral.
[00:11:26.783]The aircraft here, the fuselage and slides in to it
[00:11:32.741]and velcros into place, good Velcro.
[00:11:35.820]I'll tell you unmanned aircraft have been
[00:11:37.765]responsible for more Velcro sales
[00:11:40.247]and battery sales than I think anything else.
[00:11:45.989]Okay so sensors like I said it's the business end,
[00:11:49.472]here's the MicaSense response, the blue, green, red,
[00:11:56.666]RedEdge, and near infrared bands, and this is kind
[00:12:01.145]of one of their claims to fame I guess you might say,
[00:12:05.876]this RedEdge, that's a frequency that they've honed out
[00:12:09.684]in one of the lenses, one of the sensors they've got,
[00:12:15.470]and then the response depending if they gotta healthy plant
[00:12:21.112]or a stressed plant to this wavelength here,
[00:12:25.421]visible and then to the non-visible wavelengths.
[00:12:31.149]And this is the plant reflectance here.
[00:12:33.936]So you see there's a difference here, so the idea's that
[00:12:36.822]by looking at this you can start to tell if you've
[00:12:39.510]got healthy plant or stressed plant.
[00:12:43.008]That's really in the longer run and kind of what we're
[00:12:46.928]after is that's kind of where the gold is you might say,
[00:12:50.736]is how do you take that information and bring it together
[00:12:55.328]to really tell the story of what's happening in the field.
[00:13:01.055]Okay, now I'm gonna shift to the next main item
[00:13:04.319]on that program list, which was selected projects.
[00:13:09.537]And so the Precision Agriculture Variable Rate Irrigation,
[00:13:12.720]I've been talking about that so that'll be kind
[00:13:14.593]of our first project here and we'll get you
[00:13:17.901]a launch here of the aircraft.
[00:13:20.197]We use with that aircraft, we use a winch launch.
[00:13:23.822]So we've got a winch with a rope and it pulls it up,
[00:13:27.629]because it's pretty heavy when it's all together.
[00:13:30.527]You can't just take it and throw it.
[00:13:34.279]Let me show you here, it's gonna happen pretty fast,
[00:13:37.292]the winch will kick in and it'll pull it up,
[00:13:40.204]and then there's a parachute right here,
[00:13:42.343]that little parachute will pull the winch away,
[00:13:45.647]or the line, the winch line away.
[00:13:48.492]Okay, here it goes.
[00:13:53.980]And you saw it drop off and now it's launched.
[00:13:57.885]So we've got the aircraft in the air.
[00:14:01.988]Here's a fly by, it's just gonna come by.
[00:14:04.982]So on an autonomous flight it flies its own pattern,
[00:14:09.503]we're just standing and watching, we're good at that.
[00:14:13.382]We're better if we just keep our hands off the controls.
[00:14:18.870]Kind of kidding, Buddy here is the main pilot,
[00:14:22.380]so I gotta kid him a little bit,
[00:14:24.707]Jacob Buddy Smith, goes by Buddy.
[00:14:30.895]Now I gotta be careful because he'll jump right in
[00:14:32.875]and start feeding me back information here.
[00:14:37.482]Okay, so in aviation there's a saying, there's a lot
[00:14:41.418]of sayings in aviation, one of em is take-offs
[00:14:45.267]are optional, landings are mandatory.
[00:14:51.887]Okay, so we're gonna bring it in for a landing,
[00:14:55.321]the flaps are down and this one just slides in,
[00:14:59.405]bellies in, sensors are inside the fuselage, protected.
[00:15:04.486]That's a very efficient flier, it's got the long wings,
[00:15:08.614]it can fly for a long time, over an hour flight duration,
[00:15:12.748]so it's a nice plane, but as you can see there's quite
[00:15:16.877]a bit of work to get it up in the air and flying.
[00:15:24.086]Okay, so then we're done, we pull out the data,
[00:15:28.469]and we've got for example here is a center pivot,
[00:15:34.089]this is a variable rate center pivot up at
[00:15:37.912]the Research and Extension Center, up by Mead, Nebraska,
[00:15:42.086]and the images have been mosaiced.
[00:15:50.050]Let's see if I can get this set up right here,
[00:15:52.584]it feels like it's falling off, there.
[00:15:56.056]And you can see that here by the rough edges.
[00:16:02.888]So what we did is we took a whole lot of small images
[00:16:07.215]and put em together into a single image.
[00:16:13.416]This is one of the challenges with unmanned aircraft
[00:16:15.508]is we have a limitation on altitude, and that limitation
[00:16:19.480]won't let us see the whole field at once.
[00:16:22.519]So the process is to take a lot of pictures
[00:16:25.174]and put em together, tell you a little
[00:16:27.376]bit more about that later.
[00:16:29.489]But this NDVI then, the near end for red minus the red
[00:16:35.289]divided by the near end for red plus the red.
[00:16:37.699]That was that response.
[00:16:40.413]16 centimeter resolution over 160 acres,
[00:16:46.013]so think about that and generally we're
[00:16:49.332]running about 22 gigabytes per 160 acres.
[00:16:56.132]This was a thermal imagery over 320 acres.
[00:16:59.677]This is another area that we're flying
[00:17:03.146]out at the research farm, these are two side by side
[00:17:06.849]center pivots and so we're flying 320 acres in one flight.
[00:17:12.362]We generally fly east to west, but if the wind
[00:17:16.664]is real strong we may need to reconsider
[00:17:19.124]that orientation a little bit.
[00:17:21.058]This doesn't have quite the same resolution,
[00:17:23.226]thermal cameras are more expensive,
[00:17:28.001]and the resolution is lower, if you really wanna
[00:17:31.277]spend a lot of money you can get your resolution up,
[00:17:34.272]but we accepted a little bit less
[00:17:36.093]resolution here, 34 centimeters on this.
[00:17:42.731]Okay, so that again there we're thinking about how can
[00:17:46.651]we take that information, massage it, manipulate it,
[00:17:50.814]maybe within a geographic information system
[00:17:53.412]as layers of information to arrive
[00:17:56.451]at an early detection of moisture stress.
[00:18:02.748]Another project is Characterization of Wetlands.
[00:18:08.130]So in this case what we're looking at
[00:18:09.993]are some selected measures of wetland,
[00:18:14.921]let's say performance, or status.
[00:18:17.706]In the early spring we're gonna try to locate
[00:18:20.491]the inundated area, the water inundated area
[00:18:24.316]and measure that, estimate that surface area,
[00:18:29.788]in the early spring we're also gonna look at
[00:18:32.574]and try to estimate the length of the perimeter
[00:18:37.095]of the water inundated area, so that interface
[00:18:41.508]between the water and the land.
[00:18:45.092]In the early spring, also water fowl inventory,
[00:18:50.956]you know what's the usage of the wetland
[00:18:54.093]by the migrating water fowl?
[00:18:58.599]And then in the fall we're targeting towards
[00:19:01.597]vegetative diversity and carrying capacity going into
[00:19:06.820]the winter, that's something that is of interest,
[00:19:09.284]is what's the carrying capacity, energy capacity
[00:19:12.655]of the wetland for all the critters
[00:19:16.394]that are gonna be using over the winter.
[00:19:20.315]So here we shifted to a little bit different airframe.
[00:19:23.602]This is a vertical take-off and horizontal flight.
[00:19:29.108]We wanted to try this out, it takes up less room,
[00:19:32.382]we don't have to get the long winch line
[00:19:35.198]out and we don't need as much space.
[00:19:39.076]So here's an example of this one.
[00:19:40.820]This will just be a take-off on this Firefly 6.
[00:19:47.364]So there it takes off vertical, and this is a little bit
[00:19:50.597]of a unmanned aircraft dance going on here because
[00:19:53.801]the aircraft that's videoing this is an unmanned aircraft,
[00:19:58.366]videoing an unmanned aircraft flying.
[00:20:01.181]Now we fly in all conditions, there's a lot of wind
[00:20:03.866]sometimes out there, especially the altitude, turbulence,
[00:20:06.485]we don't really shy way from it too often
[00:20:10.533]because we wanna collect data.
[00:20:14.886]So it's getting stabilized here, gears down,
[00:20:19.549]and then when it transitions, gear goes up,
[00:20:23.790]it loses a little bit of altitude, it's not as bad
[00:20:25.974]as what it looks there, and then it flies off.
[00:20:29.095]Now once it's flying in flight mode it's a lot
[00:20:32.358]more efficient, it uses a lot less energy
[00:20:35.759]to move through the air because it's using the wings
[00:20:40.071]for lift, whereas in vertical mode it's the propellor,
[00:20:44.485]so it's consuming a lot of energy, it's not as efficient.
[00:20:48.711]So in the spring time we fly this one with
[00:20:51.317]the multi-spectral and the thermal imager on it.
[00:20:56.579]In the fall we shift to this type of airframe here,
[00:21:00.276]the good old DJI Phantom, four blades, we have this one,
[00:21:05.583]the Phantom Pro Plus, and so what we're doing here now
[00:21:11.788]is that has an optical RGB type sensor on it,
[00:21:16.813]basically capturing video or stills, we capture stills
[00:21:19.415]and here's a wetland out by Seward,
[00:21:23.940]and what we're doing is using it to generate a virtual
[00:21:27.073]3D realization of the biomass that's there,
[00:21:33.359]because we wanna get an estimate of the diversity
[00:21:36.148]and the amount, and so here we've fly multiple radiuses
[00:21:42.067]and at multiple altitudes to get all the different
[00:21:47.486]perspectives of the area of interest.
[00:21:50.245]Now we've expanded this out significantly
[00:21:53.534]as we've been developing this procedure and approach.
[00:22:00.717]So then taking that data and feeding it into
[00:22:04.652]good old Pix4D, which is fantastic software,
[00:22:09.732]it starts to then assemble the data,
[00:22:15.695]it identifies where the image was taken,
[00:22:18.581]what it's seeing and how it relates to the other images,
[00:22:23.308]and starts to bring it together.
[00:22:28.412]Now this takes a long time to do.
[00:22:30.764]Last run I think was about on the order
[00:22:33.158]of 12 hours processing time.
[00:22:37.776]And so here's a view of the wetland.
[00:22:42.593]One area, this was a smaller area, now we're going out
[00:22:45.590]with higher altitude and wider radiuses as we swing around
[00:22:50.866]to get a bigger area, our whole study area.
[00:22:56.603]Another view of it, here's the road that we're on,
[00:22:59.179]here's the truck, kind of a little bit hard to see,
[00:23:01.602]when you on this view it's quite clear,
[00:23:05.871]the screen, and just is amazing the resolution.
[00:23:15.042]And then a little bit of zooming in.
[00:23:17.491]So there's a lot of scale factors going into play here
[00:23:20.642]and so this is a multi-scale process,
[00:23:27.515]and so it's a question of looking at,
[00:23:30.194]and I can't do it here because I don't have Pix4D on here,
[00:23:33.635]but in Pix4D you can spin this around, rotate,
[00:23:37.011]zoom in, zoom out, and get a real dynamic
[00:23:40.274]view of your area of interest.
[00:23:43.421]Let's see this was after some rains we had here this fall,
[00:23:48.909]so there's some water here, vegetation here,
[00:23:53.938]and as you come down in the 3D, when you drop down you can
[00:23:58.037]see the vegetation, the heights, and variation on that.
[00:24:03.876]Another project is what I'm calling Ultra Precise
[00:24:06.424]Product Placement, so I have a question for you.
[00:24:12.765]What do you think is gonna happen when everyone
[00:24:15.050]has their own drone and sees their ecosystem,
[00:24:20.486]whatever that might be, it might be their house,
[00:24:23.902]it might be their yard, might be their project,
[00:24:27.598]from an aerial perspective?
[00:24:32.343]What do you think is gonna happen?
[00:24:39.328]I think human nature's gonna take over
[00:24:42.703]and people are gonna wanna fiddle with
[00:24:45.111]their ecosystem from that perspective.
[00:24:48.878]You see something on your roof or in your gutter,
[00:24:53.623]wouldn't you like to use an unmanned aircraft
[00:24:55.430]to get it out instead of having to get
[00:24:57.080]the ladder out and climb up there.
[00:25:00.918]Yeah, so I think people are gonna want to interact
[00:25:04.838]with the ecosystem from that viewpoint.
[00:25:08.154]So now we're going from imaging and collecting data
[00:25:12.413]that way to actual interaction.
[00:25:17.550]So let's give an example here, maybe we've got wetland,
[00:25:21.638]with some invasive species, maybe some phragmites
[00:25:25.431]that aren't welcome there, and perhaps someone used
[00:25:30.517]a DJI Phantom up looking around, oh they see that,
[00:25:35.893]might be the county wheat inspector, you don't know.
[00:25:41.087]Oh, got some patches of invasive species here.
[00:25:45.665]Maybe some cattails here, that don't belong there.
[00:25:50.776]Now, what are you gonna do?
[00:25:53.759]You've sensed it, your nice new drone, well,
[00:26:02.800]I guess you go out maybe, with the sprayer on the back
[00:26:08.553]and struggle through, and maybe slog through some wetland,
[00:26:12.278]try to kill it, maybe you get a little vehicle here,
[00:26:17.538]go out, hold the sprayer up try to kill it that way,
[00:26:21.601]not very easy, ah, here we go, this is this project.
[00:26:29.777]Calling it Ultra Precise Management, this is our monster
[00:26:34.019]unmanned aircraft here, this one pushes the limits
[00:26:36.642]on the regs, this is water, I wanna make it clear
[00:26:42.385]this is not any sort of active ingredient,
[00:26:46.083]we just are getting this going actually,
[00:26:48.391]starting to figure it out, so there's a spray nozzle,
[00:26:54.059]it actually has four sprayers and there we go.
[00:27:02.683]It weighs just under 55 pounds which is
[00:27:04.970]the threshold for regulatory purposes.
[00:27:12.135]And there you can see spray.
[00:27:16.464]You can adjust the sprayer rate, turn it up or down,
[00:27:20.288]more liters per minute, less liters per minute.
[00:27:23.826]So you know you got those invasive species out there,
[00:27:29.177]and you can bring in a full-size helicopter spray system,
[00:27:35.631]which is just gonna smash em, or maybe an airplane sprayer,
[00:27:40.728]which is gonna be huge, or bring in the appropriate
[00:27:46.033]technology and just get the area that you need,
[00:27:50.201]so Ultra Precise Management.
[00:27:54.209]Another one is Measurement of Water Quality,
[00:27:57.080]that's another project, and I'll just show you this one
[00:28:00.310]right off the bat here, this slide's easier to explain
[00:28:03.309]as it's going so this is a droid,
[00:28:06.386]this is an opticopter here,
[00:28:08.794]which has been modified, it's got some PVC around here
[00:28:12.938]and a camera around it, so we into what's
[00:28:17.619]called first person view, FPV, where you wear the googles,
[00:28:21.035]and then you can see what that camera's seeing
[00:28:23.275]as it looks down, and this is a Secchi disk here,
[00:28:27.738]which is a way to measure turbidity,
[00:28:30.499]and so the Secchi disk is dropped down in the water column,
[00:28:34.195]and once you lose the ability to distinguish
[00:28:38.041]between the white and the black quadrant separation
[00:28:44.428]points then that's your Secchi depth.
[00:28:49.802]So it's flying out her over a water body and we're in again
[00:28:58.229]the early stages here doing some prototyping.
[00:29:03.851]In this case, kind of hard to see here, but the line,
[00:29:07.291]the chain that goes down to the Secchi disk
[00:29:09.964]has depth markers on it, and so looking down
[00:29:14.492]with the video camera, with the FPV,
[00:29:17.839]you can see how deep you're going
[00:29:21.282]and also see the Secchi disk.
[00:29:25.793]So that's kind of a manual approach.
[00:29:29.919]The student on this one is also looking at taking it
[00:29:33.379]to the next level and mounting an ultrasonic sensor
[00:29:37.314]on the opticopter, so that the depth that
[00:29:42.746]the Secchi disk is below water is monitored electronically.
[00:29:50.904]So it becomes more automatic, and then of course you
[00:29:53.356]start thinking, let's put the depth in the FPV,
[00:29:56.896]the first person viewer goggles, so that the person
[00:30:00.651]can watch the Secchi disk and see
[00:30:02.540]the depth all on the same view.
[00:30:07.371]And then Bio Security and Animal Agricultural
[00:30:10.048]is another project that we're
[00:30:11.892]getting started on, getting going.
[00:30:15.297]And again here I'll just play a video because it's easier
[00:30:17.952]to describe as it's playing than
[00:30:20.152]to tell about it and then play it.
[00:30:23.539]The idea of this is, this is kind of low hanging fruit,
[00:30:27.795]and one thing about bio security for animal agriculture
[00:30:31.604]in this case, chickens, we know there's a lot
[00:30:33.985]of interest in chicken production in Nebraska now,
[00:30:36.841]is that there's vectors, there are risk factors out there,
[00:30:41.350]and one vector or risk factor for chicken production
[00:30:46.435]is water fowl, you can have let's say an avian influenza,
[00:30:50.523]and it can help kind of spread that around.
[00:30:53.715]So in this case, water bodies would be of a concern
[00:30:56.989]around the production facility,
[00:30:59.733]and the production facility here is directly below
[00:31:03.205]the unmanned aircraft, you can't see it.
[00:31:07.293]So I took the unmanned aircraft up directly over
[00:31:10.907]the building, the production building,
[00:31:13.282]and then flew it there, and just did basically
[00:31:16.182]a spin around, so here we'll spin around,
[00:31:21.457]and we see there's a water body there,
[00:31:26.236]so you can pause, and you can't see that from the ground,
[00:31:30.533]so the producer here wouldn't have been aware
[00:31:33.457]of that potential source of water fowl
[00:31:36.678]that are migrating and a potential source
[00:31:39.913]of threat to the herd, I guess, the chickens.
[00:31:48.242]Here's another one out here, again quite aways away,
[00:31:52.483]not really aware of it, so it's getting up
[00:31:56.026]and just scanning the horizon as an initial stage.
[00:31:58.867]The other thing we're doing is scanning the facility,
[00:32:02.018]the building, looking at the top of the building,
[00:32:04.326]looking for bird nests, cos that's kind of a no-no.
[00:32:07.913]You don't want bird nests around the building,
[00:32:12.015]which has ventilation systems,
[00:32:14.267]you start pulling in viruses, bacteria,
[00:32:17.430]so that was another part of the scanning.
[00:32:22.833]So just looking out over the landscape
[00:32:25.284]seeing what vectors might be present.
[00:32:30.018]This is working with a Professor in Veterinary Sciences,
[00:32:33.263]so he's the expert in that area.
[00:32:36.876]Let's see I'll pause here, and here's another one
[00:32:40.533]out here, another water body, and another, no that's
[00:32:45.122]a farmstead sorry, well there's a water source here.
[00:32:52.403]So that's kind of thinking of unmanned aircraft
[00:32:55.704]and how they can help in terms of bio security.
[00:33:03.562]Okay, another project is safety,
[00:33:06.038]and I mentioned that earlier as a third item
[00:33:08.602]in the program here, and this is a real emphasis
[00:33:12.970]for us, safety is important, and aviation,
[00:33:16.554]one of the things that you tend to develop,
[00:33:18.862]and Buddy has a real strong sense of this,
[00:33:22.656]is attention to detail.
[00:33:26.339]Attention to detail is important,
[00:33:29.016]because with an aircraft, with a piloted air craft,
[00:33:35.401]especially, but even with unmanned aircraft,
[00:33:38.074]when things happen in a way you don't want em too,
[00:33:41.782]things can happen real fast,
[00:33:44.712]so you wanna be watching the details.
[00:33:49.991]But in the case too there are other low-altitude piloted
[00:33:53.378]aircraft that we'll need to be thinking about.
[00:33:56.570]Aerial applicators, aircraft that are in this world
[00:34:02.829]are expensive, it's not uncommon to be in $1,000,000
[00:34:05.977]price range for one of those air tractor,
[00:34:09.087]a turbine air tractor flying pretty fast,
[00:34:11.232]150 to 180 mile an hour and can be difficult to see,
[00:34:17.138]especially if you're down working on something
[00:34:19.994]and they're coming over the horizon.
[00:34:22.570]I've been caught a few times,
[00:34:25.441]just like whoa, where'd that come from?
[00:34:30.089]So what can happen here?
[00:34:32.721]Well here's a side-view, a nice spray plane,
[00:34:39.287]notice the canopy here where the pilot is.
[00:34:47.253]This was a bird strike.
[00:34:51.264]See the feathers here.
[00:34:53.389]So there's some risk of collision with birds,
[00:34:55.977]the pilot survived this, most of them wear helmets
[00:35:00.390]with face shields for this reason,
[00:35:02.966]but imagine if it was an unmanned aircraft.
[00:35:08.551]Birds, their muscle, their bone, their feathers,
[00:35:12.933]unmanned aircraft are batteries, lithium-polymer batteries,
[00:35:16.797]carbon-fiber, could have been much worse.
[00:35:21.532]So, yeah this is a concern here.
[00:35:25.883]So something that we've been working on is a beacon,
[00:35:29.298]a safety beacon that would be out in the field,
[00:35:32.915]maybe where the ground control station is.
[00:35:35.726]We did testing with a number of different types
[00:35:39.243]of strobes, we elected to go with what's called
[00:35:43.219]the Whelen M-9, those are bright strobes, really bright,
[00:35:48.410]and we found red was the most effective,
[00:35:51.238]we tried white, and green, and red,
[00:35:53.702]and red gave the best distance recognition.
[00:35:58.171]And so these would be mounted on the vehicle
[00:36:02.145]where the ground control station is and any sort
[00:36:07.101]of low-flying aircraft or aircraft coming in the area,
[00:36:11.566]it would catch their eyes, let em know.
[00:36:14.476]There's a lot more to it, we won't go into
[00:36:16.509]that whole story, but I will tell you
[00:36:18.820]that these Whelen M-9s, they have about 180 different
[00:36:26.796]patterns, blinking patterns that you can program,
[00:36:32.219]so it's amazing, you can have three quick flashes
[00:36:35.811]and a pause, two quick flashes and a pause,
[00:36:39.006]three quick flashes and a pause, you know
[00:36:41.302]you set up patterns pretty much what you want.
[00:36:44.245]Well we were in the lab, Buddy and I, and we were
[00:36:48.011]trying to get all eight of em on the same pattern,
[00:36:54.339]so we're watching, and I have my eyes squinted down
[00:36:58.830]just almost closed to look at em because they're so bright,
[00:37:05.331]and then Buddy would hit the programming button
[00:37:08.340]and okay I think that's the one stop,
[00:37:11.252]and we'd go the next one and try to get em sequenced in.
[00:37:16.236]So the next day I came into the lab and I'm visiting
[00:37:21.359]with Buddy, and I say, man I don't know about you
[00:37:23.449]but I felt terrible last night.
[00:37:26.305]He said, yeah I did too, I felt terrible.
[00:37:30.026]Like you almost got the flu or something.
[00:37:33.275]It's a strobe effect and it has an effect on your brain
[00:37:40.596]and so next time I brought in welding goggles to wear
[00:37:45.580]while we were doing that sequencing
[00:37:48.100]so I don't have to try and squint down.
[00:37:50.859]But they are super bright.
[00:37:56.527]Okay, another part of safety is regulations and Buddy,
[00:38:01.402]and I have done training workshops on regulations.
[00:38:05.378]Part 107 is the regulations for unmanned aircraft
[00:38:09.155]and so this is just an example, they must weigh less
[00:38:12.658]than 55 pounds, that monster one that I showed you,
[00:38:16.212]that's about 54 an 1/2 pounds when it's filled with liquid.
[00:38:22.078]You always have to retain the unmanned aircraft within
[00:38:25.161]visual line-of-sight for the pilot in command,
[00:38:31.291]and you cannot operate unmanned aircraft over any exposed
[00:38:36.820]people that are not directly involved of the operation.
[00:38:40.837]Although, I just saw now where CNN has got a rapid approval
[00:38:45.865]process, with the FAA, they worked it out with the FAA,
[00:38:49.281]where they can fly over populated people, groups, and such
[00:38:54.769]with unmanned aircraft on an almost instant approval basis.
[00:38:59.152]In that world you can't necessarily submit your application
[00:39:03.026]and wait for two months for it to get approved by the FAA
[00:39:07.675]to do something that is outside of the regulations,
[00:39:11.329]which are called waivers, which is the typical procedure.
[00:39:14.634]So like if we wanted to fly at night,
[00:39:16.288]there's not night operations under Part 107,
[00:39:18.528]but if we decide we want to see the corn is doing out
[00:39:21.645]there at night, then we need to apply for a night waiver,
[00:39:27.865]identify how we're gonna mitigate the risk
[00:39:30.184]of flying at night and then wait a couple of months,
[00:39:33.603]and then hopefully get the approval
[00:39:35.507]letter that we can fly at night.
[00:39:39.203]CNN couldn't do that with news so they got,
[00:39:42.900]they just got it, it's just been announced,
[00:39:45.267]they got it worked out, so if you see em flying over people
[00:39:51.916]like at a Friday night football game in Milligan, Nebraska,
[00:39:56.271]they probably didn't get the approval from the FAA
[00:39:59.252]and it's probably an illegal flight operation,
[00:40:02.067]because people down below didn't necessarily
[00:40:04.794]agree to accept that risk of that thing
[00:40:07.611]falling out of the sky and maybe doing some damage,
[00:40:11.363]which you've probably all seen the YouTube videos,
[00:40:14.025]there's a lot of them out there of all kinds
[00:40:16.041]of goofy things I guess people do with unmanned aircraft,
[00:40:21.204]that go wrong, a lot of things go wrong.
[00:40:24.282]People get hurt with them too.
[00:40:26.610]There's a singer that was doing a concert,
[00:40:30.459]I don't remember who it was and he got sliced up,
[00:40:33.689]a lot of times you go to the emergency room
[00:40:35.977]after something like that.
[00:40:39.899]Part of the Part 107 is knowledge and awareness
[00:40:43.480]of the airspace that you're working in,
[00:40:46.492]the rules of aviation, because in the world of aviation
[00:40:50.875]there's a whole set of rules and a way
[00:40:52.750]of navigating the airspace, it's like getting in a car
[00:40:56.123]and driving, you wanna know how to drive safely.
[00:40:59.594]You wanna know the rules of the road.
[00:41:01.739]You got a driver's license.
[00:41:04.259]So in airspace, it's the same thing, there's different types
[00:41:07.209]of airspace, for example we have Class C airspace here
[00:41:11.704]in Lincoln, Class Charlie airspace, Class D airspace
[00:41:16.535]would be like Grand Iowa, there's some amount
[00:41:19.535]of air traffic in and out, maybe some passenger traffic,
[00:41:23.116]control tower, Class B airspace is big airspace,
[00:41:29.700]Chicago, Denver, Dallas, you know big airports
[00:41:35.114]are Class B, and then there's Class E airspace,
[00:41:40.005]and then Class G airspace, this is kind of where we are.
[00:41:44.246]Generally we're below 700 foot because the maximum
[00:41:47.789]is 400 according to Part 107 rules and so we're
[00:41:52.056]in Class G airspace, that's kind of a class,
[00:41:54.214]let's go for it airspace, kind of a way to remember that.
[00:42:01.449]So anyway, thinking about that and being aware
[00:42:04.346]of that is important to be safe and so how do you know that,
[00:42:08.085]well that's what's called sectionals.
[00:42:11.040]So a part of learning Part 107 is understanding more
[00:42:14.162]about sectionals, which is basically a representation
[00:42:18.376]of complicated three-dimensional space on a map.
[00:42:24.678]It might even be a fold out map.
[00:42:26.675]Although most people are using iPads now,
[00:42:29.789]but not too long ago it was paper maps,
[00:42:34.086]spread it out, figure out where you're at and stuff,
[00:42:37.136]well that's 3D space there, there's a lot going
[00:42:40.268]on in a sectional and so part of learning about
[00:42:44.416]Part 107 is understanding what these circles mean,
[00:42:49.194]and how to stay out of trouble basically.
[00:42:55.590]Okay, so that's kind of the first two elements
[00:43:00.854]of my program, the background and then
[00:43:04.411]the main projects including safety.
[00:43:09.997]So I did wanna talk a little bit about flight operations
[00:43:13.790]and kind of delve into that a little bit, just briefly.
[00:43:19.811]So here's an autopilot system this is called the Blackswift,
[00:43:23.519]it has a lot capabilities, in-flight limits,
[00:43:28.010]we can set up orbit points where the aircraft
[00:43:30.575]will just go off and orbit an away point.
[00:43:34.427]Also on the aircraft there's the autopilot system
[00:43:40.691]that's mounted in the aircraft, GPS, 3 axis IMU,
[00:43:45.242]now we're going to 6 axis IMUs which is kind of redundancy,
[00:43:49.330]static pressure indicated air speed for knowing
[00:43:53.736]how fast the aircraft is moving through the air.
[00:43:58.402]You start to think about what are gonna do here?
[00:44:01.049]We're gonna collect data right?
[00:44:03.719]Okay, if we're gonna be flying at a certain altitude,
[00:44:06.392]maybe let's say about 100 meters or so,
[00:44:09.796]what is the width of the sensor on the ground?
[00:44:16.206]So we start getting geometry, this was in the earlier days,
[00:44:19.585]we had to do all this, get out the old geometry books,
[00:44:22.273]and remember all that stuff, now they've kind
[00:44:24.860]of automated this a little more, and then how are we
[00:44:27.521]gonna approach the field, so let's say this our
[00:44:30.337]quarter section that we want to image,
[00:44:33.907]and we're gonna start in the southwest corner,
[00:44:36.538]we're gonna fly north, we got an 180 foot swathe,
[00:44:40.484]and we're gonna take images along the way.
[00:44:43.956]So now as the aircraft moves forward, we're gonna
[00:44:47.708]take another image, and we've got some overlap here.
[00:44:50.736]We need that overlap when we go to assemble
[00:44:53.308]all the images together go those mosaics that I showed you.
[00:44:56.892]That's how you get good coverage.
[00:45:00.435]So we what is called the race track pattern
[00:45:04.130]instead of the lawnmower pattern.
[00:45:06.233]The airplane would go up, go to about mid-field,
[00:45:09.111]come back south, go west, and then turn north a bit early,
[00:45:16.560]but yet get overlap here, see we got some overlap
[00:45:20.300]as we image going north, come back south and so on,
[00:45:24.624]and just keep this pattern going across the field.
[00:45:27.972]That's a little bit about flight operations
[00:45:29.764]and how you go about collecting this data.
[00:45:33.083]Ground control station, this is a little bit
[00:45:35.506]of an earlier version of the ground control station.
[00:45:37.690]We've got GPS wave points here, the bird is here,
[00:45:42.028]the aircraft, our home point is here,
[00:45:44.522]our ground control station located here.
[00:45:47.057]This is our first orbit point right here
[00:45:49.308]where the aircraft would orbit until we said,
[00:45:51.843]okay fly the mission, tell it to fly the mission,
[00:45:55.076]it comes out here, wave point one, it's starts flying,
[00:45:57.528]one, two, three, four, five, six, seven, eight,
[00:46:02.162]it just goes through em by number.
[00:46:07.900]And early on we kind of asked how well does the aircraft do?
[00:46:11.162]And following a trail that we want it to?
[00:46:13.755]So we come back to the lab and download the data
[00:46:16.260]from the flight computer and take a look at how it's doing,
[00:46:19.426]there's pretty strong winds occasionally where we're flying.
[00:46:22.618]We just didn't wait out the perfect days.
[00:46:26.490]And so we got some movement around our desired flight line,
[00:46:32.123]but that's why you have overlap too.
[00:46:36.364]Then we collected the thermal, visual data,
[00:46:39.544]this is a greenhouse out at the research farm,
[00:46:42.632]our thermal data here, and then I mentioned about
[00:46:45.497]how to stitch the images together.
[00:46:48.056]This is feature mapping.
[00:46:50.198]Here's two images, sequential, you can see they're not
[00:46:53.207]exactly the same, there's more distance here than here,
[00:46:56.802]and so the feature mapping goes through and starts looking
[00:46:59.800]at match points to see where the overlaps are
[00:47:01.943]and how to put those pictures together.
[00:47:05.000]The other thing we have is a log file.
[00:47:07.483]This helps out with the mapping, because we collect
[00:47:12.054]the position of every image, the latitude, longitude,
[00:47:15.548]altitude, and the role pitch in yaw.
[00:47:18.926]Basically where the aircraft is at.
[00:47:21.638]That's a metadata file that goes with the images
[00:47:24.605]and the software will use this data initially
[00:47:28.132]to start positioning, it doesn't start
[00:47:30.572]trying to find the match points right away.
[00:47:34.212]The first thing it does is position the images
[00:47:36.185]where it thinks they should be,
[00:47:37.839]and then starts working to massage it together.
[00:47:41.054]Here's an example here, six images that have been
[00:47:44.238]reconstructed using a kind of a point cloud approach.
[00:47:51.448]So here's where we're sensing, we're starting,
[00:47:55.382]we're launching, we're flying, and everyone
[00:47:58.376]of these dots is where an image was taken.
[00:48:01.024]Now this field was kind of broken into sub-sections
[00:48:04.149]where it's flying different blocks
[00:48:07.270]along the east-west direction.
[00:48:10.644]In any case we are kind of moving into the world
[00:48:14.201]of big data with 22 gigabytes, I know in some cases
[00:48:19.534]with a hyper-spectral, that one of the faculty got here,
[00:48:24.518]we're starting to talk terabytes, in terms
[00:48:27.848]of going out and flying a fairly small
[00:48:29.950]area bringing back terabytes of data.
[00:48:33.746]Okay, so data to knowledge then is what we're after,
[00:48:38.029]implies some type of synthesis,
[00:48:40.717]we gotta do something with the data,
[00:48:42.538]and maybe we're fusing the data,
[00:48:44.147]we take thermal data we're fusing it with a couple bands
[00:48:47.393]of multi-spectral or weighted bands of multi-spectral,
[00:48:50.505]there's all kinds of possibilities.
[00:48:52.424]Combination of disparate sources of information,
[00:48:55.793]perhaps using statistical methods like akriging,
[00:48:58.259]or co-kriging, and multivariate kriging,
[00:49:01.536]and maybe some of the science we can get after here,
[00:49:05.736]that's what we're all about right is philosophies
[00:49:08.556]about translation through scale in space and time.
[00:49:13.242]This a big challenge, big unknowns, so how do we move
[00:49:17.848]through scale with information?
[00:49:20.267]And this unmanned aircraft can offer another layer
[00:49:23.907]of information, perhaps to go with pilot aircraft,
[00:49:27.646]to go with satellite, and to go with field observations,
[00:49:31.034]there are all different scales in capturing
[00:49:33.361]maybe the same phenomenon but from a different perspective.
[00:49:39.975]So is or beyond science, if wanna get after fundamental
[00:49:44.266]science questions of how to move through scale,
[00:49:47.891]maybe we're also looking at good information
[00:49:50.504]to make decisions like the variable rate irrigation.
[00:49:52.938]Where am I gonna apply, how much, when?
[00:49:57.208]And then we ask about our sensors,
[00:49:59.518]are they providing good quality information?
[00:50:02.628]Validation is important.
[00:50:04.642]Are we getting true insights as
[00:50:06.673]to what we think we're looking at?
[00:50:08.842]Is it suitable scale for making a decision?
[00:50:11.784]And then are we really getting the crop stress and so on?
[00:50:16.361]These are the things that we're kind
[00:50:18.152]of looking after and pursuing in our research.
[00:50:21.767]So, I think I covered these five main areas.
[00:50:27.688]The background, went through those array of projects,
[00:50:31.384]natural resources and agriculture related,
[00:50:34.576]some aviation safety, a little bit about
[00:50:36.678]our flight operations, data to knowledge,
[00:50:39.841]and of course all this would only be possible
[00:50:42.588]through a lot of help and so there's Ag Research Division,
[00:50:48.773]USGA, EPA, CALMIT, right here, we're working with them
[00:50:54.150]on doing some calibration verification work,
[00:50:57.667]the team, the grad students, Buddy here,
[00:51:01.027]has been instrumental, retired Air Force,
[00:51:03.783]Air Guard, Colonel, you know brings great aviation
[00:51:07.198]mindset and really helps keep it all going so,
[00:51:10.813]it takes a whole effort and a lot of good support
[00:51:15.695]and help to make it go, so I wanna recognize that,
[00:51:20.023]although not necessarily in specifically, individually,
[00:51:23.021]but you know the idea, can't do it alone.
[00:51:27.389]So I'll guess I'll come back around and then
[00:51:29.616]to my hidden agenda here.
[00:51:33.379]You know what are your ideas?
[00:51:35.478]What are things that challenges you're faced with
[00:51:38.658]that maybe unmanned aircraft perspective could help
[00:51:41.725]out with, you need to get out and collect some samples
[00:51:45.406]out of a lake somewhere, or a waterway somewhere,
[00:51:50.671]you need to get up and take a look at some trees,
[00:51:53.431]and get up without having to climb em,
[00:51:55.919]or figure out another way to get up there
[00:51:58.049]and get a close in view, see if there's some
[00:51:59.980]bugs in the trees or something, I don't know.
[00:52:04.601]I'm interested in visiting further.
[00:52:07.111]Not right now, unless of course you want to,
[00:52:09.504]but if you've got a good idea hang on to it,
[00:52:11.881]I won't tell anybody I promise.
[00:52:17.321]You didn't see that did you?
[00:52:18.729]Crossed my fingers behind my back.
[00:52:21.070]No, but I hear amazing ideas all the time,
[00:52:27.510]it's just phenomenal, it's a very dynamic area.
[00:52:30.937]I mean what we started with and what we're using
[00:52:33.612]now is like night and day compared to this five
[00:52:37.000]years or so we've been at it, it's gonna continue
[00:52:39.912]to change, it's just such a dynamic area,
[00:52:43.187]technologies are going so fast, we got Google
[00:52:45.792]getting involved, Wal-Mart, FedEx, Amazon of course,
[00:52:51.922]you're probably all aware of Amazon,
[00:52:54.222]wanting to use them to deliver packages.
[00:52:57.959]One of the more innovative early package delivery systems,
[00:53:01.737]I don't know if you've ever heard about this one
[00:53:04.048]was Lakemaid Beer, Buddy's shaking his head yeah,
[00:53:10.014]that was a funny time but a fella, I think it was up
[00:53:13.945]in Minnesota or Wisconsin, for the ice-fisher people,
[00:53:18.007]out there on the lake out there doing the ice-fishing,
[00:53:21.744]he was using unmanned aircraft to deliver six-packs,
[00:53:25.190]out to em, to their ice huts.
[00:53:29.905]Lakemaid was the brand.
[00:53:33.056]He got shut down by the FAA.
[00:53:35.312]That was before Part 107 rules were published
[00:53:38.418]and they came in and said, oh no you've got
[00:53:42.043]way too much publicity and you're having too much fun,
[00:53:45.698]so you gotta stop, they interviewed them on 60 Minutes
[00:53:50.163]or something, news interview, said we were just
[00:53:52.840]having a blast but we got shut, we had to quit, we quit.
[00:53:55.427]Anyways, you got your own ideas, think about them,
[00:53:59.851]and as I hope to continue to evolve a connection here
[00:54:05.186]with the School of Natural Resources,
[00:54:06.672]maybe we can come up with some ideas, and get Buddy on the
[00:54:10.043]case and we'll solve it and make it happen.
[00:54:14.019]So I just do wanna wrap up with this plug for NU-AIRE,
[00:54:18.458]the NU-AIRE lab, the Nebraska Unmanned
[00:54:20.881]Aircraft Innovation, Research, and Education laboratory,
[00:54:25.641]and this is kind of where we're at and what we're doing,
[00:54:28.579]is trying to see where we can go with this technology.
[00:54:33.059]So I think with that I've got a few minutes.
[00:54:35.443]I'd be happy to take any sort of questions, comments,
[00:54:38.058]concerns, okay good, we have a microphone here, sorry.
[00:54:48.011][Audience Member #1] With your strobe light beacon
[00:54:51.010]for the ground control station, do you have any other
[00:54:53.851]components with that, like RF for voice announcing,
[00:54:58.485]or are you just providing that visual?
[00:55:01.495]Just visual, yeah, there's a lot of different ideas
[00:55:05.850]about how to make this connection.
[00:55:09.475]There's what's called ADSB in, ADSB out, which
[00:55:14.209]is an emerging kind of technology that is geared
[00:55:19.025]towards navigation, NextGen navigation using GPS,
[00:55:24.121]and it's also a way to kind of connect between aircraft.
[00:55:27.563]Just to let you know okay they're I'm here, be careful.
[00:55:31.917]Early on there was a view that ADSB would be a key role
[00:55:37.240]in safety for unmanned aircraft.
[00:55:43.150]I don't know how it's gonna go, because there
[00:55:46.301]could be a lot of unmanned aircraft out there,
[00:55:49.351]and if you get too many signals it can confound
[00:55:54.686]the system, you don't really know what's going on
[00:55:57.548]if there's so many around you, you can't tell
[00:55:59.997]what's happening you know so it's confusing.
[00:56:02.827]So, there's other approaches that are being advanced,
[00:56:08.188]cell phones, using cell phone towers and some sort
[00:56:11.421]of positional information that way.
[00:56:16.493]There's different ideas.
[00:56:20.116]With aerial applicators there's a culture there
[00:56:26.373]and there's a culture they tend
[00:56:28.619]to like to do things their way.
[00:56:30.903]I'd just put it that way.
[00:56:34.166]There's a lot of em that don't have transponders
[00:56:38.209]in their aircraft, they don't fly in controlled airspace,
[00:56:40.639]they don't need a transponder, so my kind of question,
[00:56:45.234]is how you gonna get em to put in another piece
[00:56:47.641]of equipment, if they won't put in transponder even.
[00:56:50.696]That's gonna cost them also, likely, you know
[00:56:53.720]it's a fee they have to pay to fly now
[00:56:56.870]and accommodate these unmanned aircraft
[00:56:59.305]that are creating problems for them.
[00:57:01.799]So we're just thinking visual, keep it simple,
[00:57:07.186]and aviation and a visual alert is a very common
[00:57:10.352]queue, if you go around an airport at night,
[00:57:13.656]you see all kinds of color lights,
[00:57:16.288]those are all different queues,
[00:57:18.024]if it's a taxiway blue, or a runway white light,
[00:57:23.127]you know coming in there's different colors,
[00:57:27.488]so a lot based on color.
[00:57:31.031]So that's kind of the thought we had, which is keep
[00:57:33.383]it simple, put it out there, it doesn't take a lot
[00:57:37.456]of FAA testing to determine frequencies and compatibilities.
[00:57:45.296]But yeah it could make progress.
[00:57:47.799]But good question thanks.
[00:57:50.059][Audience Member #2] I see you have soil moisture
[00:57:53.841]listed on your poster, could you talk a little more
[00:57:56.485]about that, is that a bit infrared,
[00:57:58.209]we've been thinking a little bit about bit of infrared
[00:58:00.714]wavelengths to do soil moisture.
[00:58:02.547]Yeah that's something that we have visited about
[00:58:08.539]and have thought about that in terms of the early season,
[00:58:12.728]you know before the crop has really taken hold,
[00:58:15.009]you know just going out and imagining the soil.
[00:58:19.128]It's interesting we were doing some visiting this morning,
[00:58:23.453]and some work this morning on some thermal data
[00:58:29.264]and we were seeing what appeared to be quite an interesting
[00:58:34.095]gradient across the field first of all in temperature,
[00:58:39.400]but it was in early May, so right away the thought
[00:58:43.682]was yeah okay that must be soil moisture,
[00:58:46.749]it's cooler, it's evaporating, and so the surface
[00:58:49.608]is cooler there, and it was interesting patterns.
[00:58:53.864]So maybe in some lower areas.
[00:58:56.550]You know in a field that may only be a few inches
[00:58:59.393]or six inches or so lower but it still
[00:59:02.723]is a depressional area, yeah that was kind of
[00:59:07.401]we were headed with that and we visited
[00:59:10.410]about just going out and applying to get maybe
[00:59:13.247]initial conditions, to start to work towards
[00:59:15.872]initial conditions, cos any modeling you do
[00:59:19.297]can be very sensitive to initial conditions
[00:59:22.519]and how you bootstrap the model and get it started,
[00:59:25.291]and where it takes off from there.
[00:59:26.889]So, yeah, good question, thanks.
[00:59:34.621]So maybe with that we'll wrap it up
[00:59:36.468]and thank Wayne for coming.
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