 Hi everyone, my name is Kim Bryson, thank you very much for inviting me to be participating in this. I'm going to be talking about drones in research in relation to agriculture, in fact in education as well. And I think my talk quite nicely segues into some of the things that Guru has been talking about because the buzz of today's agriculture is around these disruptive technologies, internet of things, big data, drone technology, smart agriculture, they're all buzz words and commonly tossed around and there's been a number of big conferences in the agricultural space and farm management space about how such technologies can be of value to the agriculture and to the whole food science issue of producing good quality food in a sustainable way. What we've done at UQ is look at how we can incorporate things like an internet of things, a multi-sensor mesh network to collect real-time biophysical data which we store in the university's cloud and we've done that around the whole thousand-odd hectares of the University of Gatton's regional campus which is a multi-enterprise agricultural centre and we've also incorporated a second multi-sensor mesh network around 10 kilometres away from the campus that will look particularly in relation to, we've set up a living laboratory there is what we called around how we can use this real-time biophysical data with drone technology to look at biomass of pasture in particular but that can end up going obviously to various crops and vegetation as well. So this network that we've set up, it's very flexible, we work with technology from libellum, we can look at different types of communication protocols so we're not just looking at Wi-Fi but we're looking at other types of radio interfaces that allow us to transmit data in real-time over long distances and through buildings and trees etc. And it's a network that is basically self-healing and we have a number of these nodes set up across the campus, here we go, smart agriculture, smart water, smart environment and smart security, this last one being the smart security one being essentially the ability through the network to turn things on and off or open and shut gates which of course is really important from an agricultural perspective. The agriculture one looks at, well in fact I think my next slide looks at the types of data that you can gather from these sensors when it's installed and we have all of these nodes and most of these sensors around the place. So we're collecting a lot of biophysical raw data in real-time which we store in the cloud and have a dashboard to essentially access. The idea is that this is done through open source so that at the end of the day people can have access to this so turn may be interested in it, other people may be interested in using it remembering it's subtropical information really for people who are interested in the agricultural space but we're looking to certainly make that available at the moment through eduroam to people who are involved in eduroam rather than anything else. We've tried it on an open network and that we found that it had a risk of the university being hacked through our system so we closed it down from worldwide access to eduroam access. So lots of data biophysical data coming in and here's a diagram that tells you that we've got data coming in from these various nodes into our cloud and then we're using that data within the educational environment now to develop various essentially data uses. So we've got research projects in here but we've got farm operation that is something that we want to do because we'd like this to be developed far more into the agricultural farm space and of course our education component. Type of data you can get you can modify all of this through our data dashboard or you can look at a QR code and get through to it. So that's the start but when we have this large amount of biophysical data what else do we need and what for and of course we want the data for things like passion monitoring and management, animal monitoring and management, crop monitoring and management and education and more data. We want to get more data. So drones provide that or satellite data or remote sensing data provide that capability of putting aerial data on top of our essentially ground and ground data collection system and if you go back to the future or we're going back to the future in this because remote sensing has been around in agriculture since the early 1970s and the problem for ag in relation to remote sensing were two main data. One was data, cost of acquisition and processing, the revisit frequency, the things that Guru has talked about actually and also a lack of skills available in the agricultural sector for this type of data to be used efficiently well in a cost effective manner and agriculture people or people in agriculture can be very easily turned off technology. You know what is the point of technology if it doesn't deliver me a cost benefit in terms of what I'm doing and if it costs me a lot and it goes wrong and I can't do it when I want to do it then it's not worth it. So we needed to do something about this lack of skills and what we've done here is look at developing the special skills that are needed to understand spatial variability in agricultural remote sensing. We've set up an agricultural remote sensing lab here at the campus and what we're doing there is we're trying to integrate students from different disciplines across the academic world so engineering students come down and work on agricultural projects in relation to things like building drones and understanding what drones can be used for, building sensors and understanding what those sensors can be used for in agricultural monitoring in particular but that does involve of course environmental monitoring. So our waste water management site is probably one of our most recorded biophysical data sites and we get them to do hands-on work so they understand the issues and the risks. We want to use them because they're cheap platform to carry high resolution sensors you can see here a spatial variability in a paddock at ground level you can see a bit but when you look over here at an aerial photo of that paddock the variability is huge and that means dollars to a farmer so we want to collect that data so we can optimize production efficiency and quality and we then also want to minimize risk and environmental impact so drones from the perspective of an agricultural person could enable us to do better in the smart food production game almost certainly enables us to do better in the smart environmental management game and for us as educators smart skills development because it's lots of skills involved and it's fun to do and one of the things we have in the Australian sector actually it's worldwide if difficulty in getting students into the agricultural industry sector and I'm meaning across the board not just you know grains or horticulture but across the board. So at UQ we've got five DJ Phantoms that we use for teaching we've got four bespoke quadcopters we've got three bespoke hexacopters and 10 mini agricultural drones which are the ones nowadays because these are the ones that I've talked about the 10 mini ag drones are the ones that we use and I'll just talk a little bit about them in the next few minutes they're easy to fly and there is an article out in 2015 about what why we're doing this and what we're doing. The design and build principles around this business of getting students of all disciplines to understand what they're doing we go through a design process trial and error using open software to look at designing these things we purchase the parts for them we they build them they test fly or they learn to fly learn to solder things like this undertake the project and write a report. So this is a classic problem-based learning or active learning scheme that we can make get students to do. Here are the types of drones that we've investigated and used over time you can see there's certainly date range here and as much as anything else this small mini UQ mini ag drone we designed and built because we under we want to fly under the two kilo limit of CASA because if we had to get every student certified we couldn't do this so this little mini ag drone which I'll talk about in a bit more detail a little bit down the track is the one that we're now flying and it this with a camera that looks at getting a normalized difference vegetation index image is less than one kilo. So the first student project was in 2013 where the student literally took a DJI Phantom with a little normal red green blue visual camera back to Fiji this was his school in 2003 this is a Google map this is his school in 2013 which is the image that you can see outlined here and he literally calculated the difference in mango clearing basically so he looked at change in mango so this was the first project that we looked at then we started getting a little bit more creative we developed a drone that could carry a multispectral camera and we started looking at prickly acacia which is a serious weed environmental weed up in central Queensland big problem from an agricultural perspective because it shuts out cattle from using the pasture underneath and we compared some of that data to satellite data and found of course that these drones creating a better resolution on the ground gave us more data. This beneficial bug drone a hexacopter weighing around about 2kg more when we've got this white thing underneath it designed by an agricultural science student which is now going to the has been invited to be put on display at the science and technology museum in London for the next five years in their display on innovations in agriculture over time so this is actually an old innovation now this is about 2013 2014 but it is an instance of when people started looking at using drones so this is a box full of beneficial bugs which the farming industry the farm group that we worked with wanted to see if they could develop drop beneficial bugs these bits coming out into the middle of a paddock instead of as they customarily do drive around the outside and so this was a very successful product project with industry involvement that then started really introducing this sort of technology from the education from the classroom into the actual agricultural space so that was great. The net drone was another one where we were asked to fly under hail netting for one of the largest seedling producers in Australia and here is the netting you can see it's a lovely picture if you appreciate it this sort of color variation the drone is flying here and it was called the net drone project we were looking to see which one of these seedlings in you know 250,000 small tube plants were germinating or not and here you could see this sort of resolution that you could pick up from a standard multi-spectral camera you could see the two-coddeliden stage here and you could introduce students to the idea that you are looking at three different bands so a little bit of physics you're looking a little bit of spatial variation because you can see where in this one where my cursor is where things haven't grown and then when you go up here at two-coddeliden stage you get that interest for a student as much they can actually see a plant germinating and they can see where it has not and what you do is you get them to calculate what the cost is to both the supplier and the buyer when they get a tray of seedlings and something hasn't germinated so that links it to the economics of the producer here is our mini-ag drone we've been flying it now for a couple of years we've used it at the beginning of this year for 35 students to do some projects it's based on Raspberry Pi technology we've upgraded it because we found that the GPS wasn't crash-hop but in this sort of thing I've got 10 drones which might cost me a hundred bucks each to repair but that means that students can crash them and that's that's good for a student to be able to do that and over here you can see them flying in on campus sort of data that you can get out of this quite basic no IR or an NDVI data camera is you can pick up this information quite nicely at an operational scale so again I'm trying I'm guess I'm not at that I'm not trying to get these students to be as knowledgeable as we might think from a research perspective but an undergraduate level 3 agricultural science student they go out with this knowledge and will then be able to develop their skills in the real world after that we are developing various sensors that can fit onto this small camera so the most recent one is a multispectral sensor again multiplexed Raspberry Pi cameras that enable us to choose very high-grade research filters of specific wavelengths when we're wanting to look at something and this particular camera at the moment is being used on top of one of our IOT multisensor mesh network to collect aerial data of the biomass of that paddock across which of some 50 hectares I believe that has a multi-sensor mesh network on it so this camera is is is now working so this was when it was being developed you can see how it's been set up for little Raspberry Pi cameras and we use Python to make sure everything goes well this is a drone that is no longer flying but it was an attempt at us delivering a drone that could pick up the RFID tags in a cow's ear from a distance and it works at about 20 meters but you know we're talking a big drone now with a big antenna and whilst it worked from an operational perspective and from the idea of trying to keep things down to a level where we're not flying above CASA's regulations this became essentially unusable and although I have an engineering student who wishes to try and reduce this in size this is something that we're not at the moment moving forward to but it is an idea it's an example of some of the ideas that come when you start talking to the industry players and asking them what they want they they wanted something like this because normally you have to stand behind beside the cow or the cow has to go through some cattle gates and literally swipe it with a hand swipe but you've got you know a thousand head of cattle that's a really difficult really time-consuming thing to do so this was about trying to improve the efficiency of being able to look at these cows in in the paddock and the sort of data we could get from it was was was really quite good so what's the types of research and learning involved is electronics and avionics which is not normal in an agricultural sense but it's really useful if you're going to get into this side of spatial variability analysis the design and build side of things we look at programming physics maths and chemistry of course because we're looking at the electromagnetic spectrum things like spectral indices and crop growth indices and chemicals and plants so if you're trying to engage a student in learning about maths which they really don't want to do you bring in something like this and it changes their perception of both learning about the maths and then how it can be used in real life when it would appear to be from a classroom perspective completely dry and equation driven. Plant physiology animal welfare and food traceability are all issues around management that we can also talk about when we use these tools and most recently we've had these sorts of projects going on drone and sensor development for crop monitoring and diseases computer vision development for monitoring pests basically but we could be doing it in the piggery or we could be doing it out in the environment for say wildlife people we've got a master of engineering student looking at developing a robotic arm for capturing these small drones out of the sky for autonomous recharging again it's an academic project probably not to not to be used operationally because of the issue of autonomous flight but very useful for the master engineering student to understand why we want that from an agricultural perspective so we want to increase obviously the reach of our drones flying and then we've had people developing automated underwater cameras to monitor phytoplankton which we can then link to our IOT and and our aerial imagery of the lakes obviously the IOT has applicability elsewhere that may not involve drones like in the equine health area and the development of this visualization dashboard for our monitoring purposes and for staff to access and as I say anyone on EDURO to access some of this data that we're collecting and as I say this is a project the RFID monitoring of cattle is something that would that we're still sort of looking at but the idea is to reduce it in size as the electronics become smaller and we don't have to have such a big antenna involved which means a bigger drone and more weight etc and start starting to contravene cattle regulations so we're looking at a whole range of things both from an education and research perspective but mainly the research is about getting is about interesting students in learning about this stuff which I think is going to be key down the track in terms of capability development for our industry both agriculture and environment or managed landscapes because without these kids coming in we're going to run out of people in terms of as they get older they're not going to be able to continue to do this so that's my presentation and I hope you enjoyed it thank you very much