The use of an unmanned aerial vehicle as a remote sensing platform in agriculture*
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    The use of an unmanned aerial vehicle as a remote sensing platform in agriculture* The use of an unmanned aerial vehicle as a remote sensing platform in agriculture* Document Transcript

    • 139 The use of an unmanned aerial vehicle as a remote sensing platform in agriculture* TA Jensen† National Centre for Engineering in Agriculture, Faculty of Engineering and Surveying, University of Southern Queensland, Toowoomba, Queensland LC Zeller Department of Employment, Economic Development and Innovation, Toowoomba, Queensland AA Apan Australian Centre for Sustainable Catchments, Faculty of Engineering and Surveying, University of Southern Queensland, Toowoomba, Queensland ABSTRACT: One of the limitations of using hobbyist remotely controlled aircraft with an attached digital camera is that a great number of images look alike and unless a large number of natural features or artificial targets are present at the location, it is hard to identify and orientate the images. This paper investigates the use of an unmanned aerial vehicle (UAV) for use in agricultural applications. Trials were conducted, in collaboration with researchers from the Australian Research Centre for Aerospace Automation and Queensland University of Technology, on the ability of a UAV autopilot to accurately trigger a two-camera sensor when at a desired location. The study area was located at Watts Bridge Memorial Airfield, near Toogoolawah (152.460°, –27.098°) in southeast Queensland, Australia. The airfield has dedicated areas for use of remotely controlled aircraft, with the mission being undertaken on 5 March 2008. The target and waypoints were arranged so that the UAV flew in an anticlockwise flight pattern. Three separate missions were flown with images being acquired when over target on each of the nine passes. Although capturing the target in the image was achieved on every flight, the accuracy of capturing the target in the middle of the image was variable. The offset from the centre of the image to the target (zero in the perfect system) ranged from just under 15% to just over 60% of the image extent. The misalignment was due to a combination of the predetermined offset value, cross-wind, global position system/autopilot error, the UAV not being level when the image was acquired, and/or inaccuracies in positioning the sensors in the hinged pod. The capacity to accurately acquire images over pre-determined points is essential to ensure coverage and to expedite mosaicing of the images. It will also expand the application of these technologies into the broader-scale applications, such as imaging in broadacre cereal cropping or imaging along transects. 1 INTRODUCTION search and rescue (Goodrich et al, 2008); a very expensive and sophisticated UAV (the NASA- The use of unmanned aerial vehicles (UAVs) as developed Pathfinder) was used for coffee ripeness remote sensing tools is not new, with a number of monitoring (Johnson et al, 2004); and UAVs were recent studies detailing various applications: off-the- used to monitor wheat in small plots with colour shelf componentry was used to construct a UAV for and near-infrared (NIR) images acquired to develop rangeland photography (Hardin & Jackson, 2005); relationships between vegetation indices and field a camera-equipped UAV was used for wilderness measured parameters (Lelong et al, 2008). The above remote sensing methods used either real- * Reviewed and revised version of a paper originally presented at the 2009 Society for Engineering in Agriculture (SEAg) National time monitoring of the acquired imagery, or required Conference, Brisbane, Queensland, 13-16 September 2009. a large number of images to be taken with the most † Corresponding author Dr Troy Jensen can be contacted at appropriate being selected for future analysis. No troy.jensen@usq.edu.au. previous studies have investigated the capacity of a © Institution of Engineers Australia, 2011 Australian Journal of Multi-disciplinary Engineering, Vol 8 No 2N11-AE06 Jenson.indd 139 25/11/11 2:12 PM
    • 140 “The use of an unmanned aerial vehicle as a remote sensing platform ...” – Jensen, Zeller & Apan UAV to acquire an image only when at a particular 2 STUDY AREA location (set of coordinates). The ability of a UAV to navigate to a particular set of coordinates was, The study area was located at Watts Bridge Memorial however, detailed in a paper investigating spore Airfield, near Toogoolawah in southeast Queensland, collection from the air (Schmale III et al, 2008). These (152.460°, –27.098°), Australia (figure 1). The mission investigators utilised the global position system (GPS) was undertaken between 1300-1600 hours on 5 March track log to determine the flight path of the UAV. 2008. A slight breeze only became evident in the last hour of testing. The purpose of this investigation was to evaluate a fully autonomous image acquisition system. To achieve this objective, the ability of the autopilot to 3 PLATFORM trigger a remote sensing camera system was tested, and the three-dimensional accuracy of the autopilot To undertake this evaluation, a specially modified (x, y, z) was also evaluated. The procedures to version of a remotely-controlled “Phoenix perform this testing and evaluation are detailed in Boomerang” 60-size fixed-wing trainer aircraft this paper. fitted with an autonomous flight control system was Figure 1: Location of the Watts Bridge Memorial Airfield. Australian Journal of Multi-disciplinary Engineering Vol 8 No 2N11-AE06 Jenson.indd 140 25/11/11 2:12 PM
    • “The use of an unmanned aerial vehicle as a remote sensing platform ...” – Jensen, Zeller & Apan 141 utilised (Model Sports, n.d.). The platform consisted in Jensen et al (2007). This investigation utilised 5.0 of two 60-size Boomerangs merged together to megapixel Kodak Easyshare CX7525 (Kodak, n.d.) give a wingspan of just under 3.0 m. The platform digital zoom camera (Eastman Kodak Company, (figure 2) was powered by an “OS Engines” 91FX Rochester NY). As described in the previous work, (16 cc) (O. S. Engine, 2011) methanol-glow motor. the two-camera system (one camera to capture the The baseline avionics on the platform included the colour and the other the NIR portion of the spectrum) “MicroPilot MP2028g” autopilot (MicroPilot, 2011) was remotely triggered, and was sensitive to NIR and a “microhard Systems Inc. Spectra 910A” 900 light (once the NIR cut-out filter had been removed). MHz spread spectrum modem (Microhard Systems The system was housed in a streamlined pod Inc., 2011) for communications with the ground attached to the underside of the fuselage directly control station. The speed range of the platform was beneath the wing. The pod was hinged for easy from 45-120 km/h with cruise speed being 70 km/h. access and download of the cameras (figure 3). As The payload of the platform was 3 kg and had a fly the sensor had been previously triggered using a time of 25 minutes. spare output channel of the radio control equipment, this was easily adapted to suit the autopilot system. 4 THE REMOTE SENSING SYSTEM When the UAV was within a predetermined distance of the designated location (set prior to take-off at The remote sensing system used to acquire images 20 m to allow for cross-winds, GPS error and camera was based on the system developed and detailed misalignment), the autopilot set a spare servo channel Figure 2: The Queensland University of Technology UAV ready for take-off. Figure 3: The pod opened to remove the secure digital (SD) cards from the sensors. Australian Journal of Multi-disciplinary Engineering Vol 8 No 2N11-AE06 Jenson.indd 141 25/11/11 2:12 PM
    • 142 “The use of an unmanned aerial vehicle as a remote sensing platform ...” – Jensen, Zeller & Apan to the maximum output for 600 ms. A microcontroller higher (hence perceived safer) altitude and likewise (PICAXE-08; Picaxe, n.d.) was programmed to detect to simulate flying over obstructions and coming this change of state on the designated channel and down to image acquisition height. Once past the trigger both cameras. The time delay between the target, the UAV resumed normal flying height. After trigger signal and the shot being taken was 0.5 s 15-20 minutes of autonomous flying, the UAV was with a further 1-2 s being required for the image to manually landed and the flight log was downloaded be stored on the memory card. The microprocessor from the autopilot. also gave both cameras a pulse every 10 s to ensure The flight log contained 52 columns of information, that they did not power down. recorded at 5 Hz. The log contained information detailing the state of the aircraft and included 5 DEPLOYMENT attributes such as attitude, position, speed, heading and servo values. Four flights were undertaken on The UAV was programmed with a flight plan the day of testing with images successfully captured to do a number of left circuits over a series of on three of these. The second flight had to be aborted pre-determined waypoints (figure 4). One of the dots and the UAV landed immediately, as conventional is brighter, indicating that this is the next waypoint aircraft came into the proximity of the UAV. The that the UAV is heading towards. When passing imagery acquired was analysed to provide flight above the origin point (the target of the image path accuracies. acquisition and where the UAV was initialised), the autopilot triggered the cameras. 6 RESULTS AND DISCUSSION The take-off of the UAV was performed manually, under the visual control of a radio-control pilot. A flight path of the three successful missions is shown Upon reaching a safe altitude (30 m), the UAV was in figure 5. Two circuits were completed on flights switched into autonomous mode and the autopilot one and three, with three circuits being made on flight started guiding the aircraft along the set track, with four. Each dot in the circuit represents the latitude flight height targeted at 120 m above ground level and longitude of the path taken by the UAV that was (AGL). When the UAV approached the imaging target updated by the GPS every second and recorded in (the initialisation point) the UAV was instructed to the flight log. The activity around the target area change altitude to 90 m AGL. The change in altitude and the reduced distance between consecutive dots was performed so that most of the flight was at a in this area indicates that this was the take-off and Figure 4: The Horizon Flight Schedule ground control station software showing the path and the flight details of the UAV being monitored in the autopilot flight software. Australian Journal of Multi-disciplinary Engineering Vol 8 No 2N11-AE06 Jenson.indd 142 25/11/11 2:12 PM
    • “The use of an unmanned aerial vehicle as a remote sensing platform ...” – Jensen, Zeller & Apan 143 Figure 5: The flight paths and target positioning, Watts Bridge on 5 March 2008. (a) (b) Figure 6: (a) Image 100_4814 taken at 3:27:44 pm. (b) Image 100_4815 taken just over 3 minutes after image 100_4814. landing zone. The flight path is superimposed over The target and waypoints were arranged so that the a Spot 5 satellite image showing the infrastructure UAV should in theory fly directly down the centre of the Watts Bridge Memorial Airfield and other of the mowed grass runway that ran northeast- natural features in close proximity. Also displayed southwest in figure 5. This should have resulted in are the waypoints used in determining the flight the runway being positioned vertically in the centre path and the location of the target, over which the of each image acquired. This was not the case. The images were captured. misalignment was possibly due to a combination of cross-wind, GPS/autopilot error, the UAV not An example of two of the images captured on flight being level when the image was acquired, and/or four are shown in figure 6. These images were taken inaccuracies in positioning the sensors in the hinged on the last circuits made by the UAV on the day. pod. Defining the errors and refining them was not Even though the images were acquired a little over within the scope of this proof-of-concept research. 3 minutes apart, there is good consistency in the coverage and positioning of the target within both Flight details and inaccuracies in the image acquisition images. Ideally, if the autopilot was doing a perfect were quantified and detailed in table 1. The scale of job guiding the UAV, the target should be in the centre the images were determined using GPS coordinates of the image. As can be seen from the images (figures of known features on the images. The direction of 6), this was not quite the case. flight of the UAV was from the top of the image to Australian Journal of Multi-disciplinary Engineering Vol 8 No 2N11-AE06 Jenson.indd 143 25/11/11 2:12 PM
    • 144 “The use of an unmanned aerial vehicle as a remote sensing platform ...” – Jensen, Zeller & Apan Table 1: Details of the errors for the images acquired over the target. Altitude Heading Image offset Image extent Area Image # Time ASL (m) (degrees) X (m) Y (m) Absolute X (m) Y (m) (ha) Flight 1 100_4801 1:32:06 261 150 50.4 8.3 51.1 140.4 104.0 1.46 100_4803 1:35:26 83.7 17.8 85.6 133.4 98.8 1.32 100_4805 1:38:32 59.6 10.2 60.5 137.4 101.8 1.40 Flight 2 100_4806 2:36:10 aborted mission, no data collected Flight 3 100_4809 2:57:24 216 146 17.2 –1.9 17.3 103.0 76.3 0.79 100_4810 3:00:42 198 132 19.1 0.3 19.1 92.2 68.3 0.63 Flight 4 100_4812 3:21:48 211 113 39.5 6.0 40.0 108.7 80.5 0.88 100_4813 3:24:46 38.2 26.6 46.5 92.0 68.2 0.63 100_4814 3:27:44 192 114 –19.3 14.1 23.9 100.4 74.4 0.75 100_4815 3:30:50 166 125 –9.6 4.9 10.8 73.9 54.7 0.40 the bottom. In the image offset column in table 1, the response of the UAV to changes of the flight schedule. X distance is the cross-track distance with a positive An altitude plot of flight 4 is shown in figure 7, value indicating that it is to the left and negative showing the relatively steep climb of the UAV after to the right of the centre of the image. The offset take-off. Also evident is the loss of altitude, and then in the direction of flight (undershoot or overshoot) correction, due to the banking of the aircraft when is indicated by the Y column with a positive value manoeuvring to align to the next waypoint. The indicating that the image was captured before the saw-toothed nature of the plot, due to the banking, centre of the image with a negative value indicating indicates that the feedback loops to the autopilot to after capture. The absolute is the direct distance from control the flight surfaces are not finely tuned enough the centre of the image to the centre of the target. to optimise performance and ensure stable flight. No Capturing the target in the image was achieved on attempt was made to refine the triggering accuracy every flight. However, capturing the target in the in this preliminary study. However, modifications middle of the image was not as repeatable with the such as; extending the turn area to ensure the aircraft error ranging from just under 15% of the image width was in straight and level flight when the camera was (10.8 in 73.9 m; the final image on flight four) to just over target and triggered, or a more stable airframe over 60% of the image width (85.6 in 133.4 m; the such as an electric glider-style, may have improved second image of flight one). on the results obtained. The capacity to accurately acquire images over pre- determined points is essential to ensure coverage 7 CONCLUSIONS and to expedite mosaicing of the images. It will also expand the application of these technologies into This study provides proof-of-concept that a low-cost the broader-scale applications, such as imaging in auto-piloted UAV can fly on a pre-determined path broadacre cereal cropping or imaging along transects and acquire images at pre-determined locations. On (such a river systems, etc.). every attempt, the target was successfully captured in Differing altitudes were programmed for each flight the images. The proximity of the target to the centre (table 1). The first flight was undertaken at 250 m of the image varied due to a number of factors such above sea level (ASL) with the third at 204 m. The as wind speed, direction, aircraft attitude and GPS/ final flight was slightly different. The first image autopilot/camera lags. Improving on the accuracy of was acquired at the set altitude of 214 m. The three the image acquisition was beyond the scope of this circuits that followed were flown at this set height; initial evaluation, however some simple measures however the images were acquired at lower altitudes such as ensuring straight and level flight at image (194 m for images two and three, and 169 m for the acquisition, a more stable platform, careful orientation final image). These image acquisition heights were of the camera and a higher update rate GPS would changed in-flight with the intention of observing the have a beneficial effect on the accuracies obtained. Australian Journal of Multi-disciplinary Engineering Vol 8 No 2N11-AE06 Jenson.indd 144 25/11/11 2:12 PM
    • “The use of an unmanned aerial vehicle as a remote sensing platform ...” – Jensen, Zeller & Apan 145 Figure 7: Altitude details for flight 4 (note the UAV reduced altitude to acquire images). This autonomous system has the potential to Applied Engineering in Agriculture, Vol. 20, No. 6, pp. be a highly suitable platform for “real world” 845-849. applications, but needs further development to overcome the accuracy issues. As the capacity to Kodak, n.d., “KODAK Digital Cameras, Printers, perform automatic registering and mosaicing of the Digital Video Cameras & more”, www.kodak.com. acquired images filters down from conventional aerial imagery, this low cost remote sensing system Lelong, C. C. D., Burger, P., Jubelin, G., Roux, B., will have great potential to be utilised in broader Labbe, S. & Baret, F. 2008, “Assessment of unmanned agricultural applications. aerial vehicles imagery for quantitative monitoring of wheat crop in small plots”, Sensors, Vol. 8, No. 5, pp. 3557-3585. REFERENCES Microhard Systems Inc., 2011, “Leaders in Wireless Goodrich, M. A., Morse, B. S., Gerhardt, D., Cooper, Communication”, http://microhardcorp.com. J. L., Quigley, M., Adams, J. A. & Humphrey, C. 2008, “Supporting wilderness search and rescue using a camera-equipped mini UAV”, Journal of Field Robotics, MicroPilot, 2011, “World Leader in Miniature UAV Vol. 25, No. 1-2, pp. 89-110. Autopilots since 1994”, www.micropilot.com. Hardin, P. J. & Jackson, M. W. 2005, “An unmanned Model Sports, n.d., “Welcome”, www.modelsports. aerial vehicle for rangeland photography”, Rangeland com.au. Ecology and Management, Vol. 58, No. 4, pp. 439-442. O. S. Engine, 2011, “O.S. Engines Homepage”, www. Jensen, T., Apan, A., Young, F. & Zeller, L. 2007, osengines.com. “Detecting the attributes of a wheat crop using digital imagery acquired from a low-altitude platform”, Picaxe, n.d., “Home”, www.picaxe.com. Computers and Electronics in Agriculture, Vol. 59, No. 1-2, pp. 66-77. Schmale III, D. G., Dingus, B. R. & Reinholtz, C. 2008, “Development and application of an autonomous Johnson, L. F., Herwitz, S. R., Lobitz, B. M. & unmanned aerial vehicle for precise aerobiological Dunagan, S. E. 2004, “Feasibility of monitoring coffee sampling above agricultural fields”, Journal of Field field ripeness with airborne multispectral imagery”, Robotics, Vol. 25, No. 3, pp. 133-147. Australian Journal of Multi-disciplinary Engineering Vol 8 No 2N11-AE06 Jenson.indd 145 25/11/11 2:12 PM
    • 146 “The use of an unmanned aerial vehicle as a remote sensing platform ...” – Jensen, Zeller & Apan TROY JENSEN Dr Troy Jensen is a Senior Research Fellow/Senior Lecturer with the National Centre for Engineering in Agriculture and the Faculty Engineering and Surveying, University of Southern Queensland (USQ), Toowoomba. He received his PhD degree in Engineering from USQ. Applying engineering technologies to agriculture is something that Troy has been doing since be commenced work in 1987. Since this time, he has gained extensive applied research experience in such diverse areas as agricultural machinery, animal and plant biosecurity, precision agriculture, remote sensing, controlled traffic farming, native grass seed harvesting and management, grain storage, horticultural mechanisation, and biomass reuse. His current research area focuses on the use precision agriculture technologies, and is working on a project funded by the Sugar Research and Development Corporation titled “A co-ordinated approach to Precision Agriculture RDE for the Australian Sugar Industry”. LES ZELLER Les Zeller is a Senior Research Engineer with the Queensland Department of Employment, Economic Development and Innovation based in Toowoomba. He received his Associate Diploma and Masters Degree in Engineering from the University of Southern Queensland and his Bachelor in Applied Physics from Central Queensland University. He has worked in agricultural research for over 30 years in the development and application of electronic technologies for agricultural research. A turf traction measurement device he developed has been patented by the Queensland State Government. ARMANDO APAN Dr Armando A. Apan is an Associate Professor with the Australian Centre for Sustainable Catchments and the Faculty of Engineering and Surveying, University of Southern Queensland, Toowoomba. He received his PhD degree in Geography from Monash University in Melbourne. His current research area focuses on the use of remote sensing and geographic information systems in environmental management, agriculture, forestry and ecology. He was awarded the Queensland Spatial Excellence Award (Education and Professional Development) in 2006 by the Spatial Sciences Institute, Australia. Currently, he is the Associate Dean (Research) of the Faculty of Engineering and Surveying, University of Southern Queensland. Australian Journal of Multi-disciplinary Engineering Vol 8 No 2N11-AE06 Jenson.indd 146 25/11/11 2:12 PM
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