Page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Page 7 Page 8 Page 9 Page 10 Page 11 Page 12 Page 13 Page 14 Page 15 Page 16 Page 17 Page 18 Page 19 Page 20 Page 21 Page 22 Page 23 Page 24 Page 25 Page 26 Page 27 Page 28 Page 29 Page 30 Page 31 Page 32 Page 33 Page 34 Page 35 Page 36 Page 37 Page 38 Page 39 Page 40 Page 41 Page 42 Page 43 Page 44 Page 45 Page 46 Page 47 Page 48 Page 49 Page 50 Page 51 Page 52 Page 53 Page 54 Page 55 Page 56 Page 57 Page 58 Page 59 Page 60 Page 61 Page 62 Page 63 Page 64 Page 65 Page 66 Page 67 Page 68 Page 69 Page 70 Page 71 Page 72 Page 73 Page 74 Page 75 Page 7621 IREC Farmers' Newsletter No. 195 ­ – Rice R&D 2016 Drone Deploy, which automate the flying process. The main picture shows Drone Deploy (www.dronedeploy.com) being used to automate flights over a given field. It is as simple as clicking on field boundaries, setting the flight altitude and the app then takes over the control of the drone, selecting the best flight paths to optimise image collection. Once the drone lands, the captured images are downloaded from the camera, and uploaded to the internet. Cloud (internet) based apps such as Drone Deploy or Micasense Atlas then process the collected images, automatically stitching the multiple images together to provide a single high-resolution image of a whole field, and then automatically providing calibrated vegetation index images to the user. These images can then be shared between farmers and agronomists or other decision makers. Costs for these services are in the range of $30 per month, or can be based off a fee per hectare, depending upon what best suits the user. What are the benefits? One of the major advantages of using a low-cost drone platform is the ability to take images when wanted. Farmers don’t have to wait for a neighbour or a scheduled plane flight. If they want an image before, during or after an irrigation, they have the ability to collect this data on the spot. The second major advantage is the high resolution of these images compared with traditional satellite or plane based platforms. Rather than traditional satellite images, which generally feature tens of metres resolution, drone-based data is generally around 5–10 cm, allowing individual plant data to be collected. This has potential benefits for identifying weeds and allows impacts of events such as soil compaction from wet harvests to be seen in collected images. Additionally, cloud cover does not obscure image capture, which is sometimes an issue with satellite imagery. Figure 2 shows an NDRE image collected from the drone-based platform shown in Figure 1. NDRE has been shown to be strongly related to chlorophyll concentration and therefore to plant nitrogen content. From this image, taken after topdressing, the effects of non-uniform spreading of urea from the spreader can be seen from the green/yellow striping. Figure 3 shows late season rice crop images taken in late March. The variable crop maturity across the block and bays can be seen, along with the impact of the variable urea spreading, which is still evident. This variability in maturity will have an effect on final yield and grain quality and can be traced back to previous management decisions or effects from fertiliser, layout, soil type and levelling. This variability data can be used to optimise farm management and rectify the variability in the future. Customised input management based on this variability assessment has the potential to enable farmers to increase yields and farm profitability. As a side note, the impact of the old contour layout can also be seen in Figure 3, particularly in the lower right of the image. These areas are generally maturing faster. Insights such as these are difficult to see with coarse resolution images such as those from satellites. What does the future hold? As can be seen, the information collected from these drone-based platforms can be used to gain new insights into the effects of water and nitrogen management on plant growth. Strategies can be developed to maximise yields across the farm, based on analysing the best and worst performing areas. The technology is now at a price point that is affordable. The automation and simplification of the data collection process will allow greater uptake by growers. For those who would like further information, please contact us (see details following). Additionally, consider registering for the Deakin Drone Day to be held by Deakin University at Hanwood in October, where a range of drones, sensors and software will be demonstrated. It will provide hands-on use of a range of drones and sensing platforms and discuss data processing and subsequent decision making. RIRDC Project PRJ-010400 Changes in rice water use – scoping study Acknowledgement The project would like to acknowledge the funding provided by the Rural Industries Research and Development Corporation. More information John Hornbuckle M: 0429 862 920 E: [email protected] Twitter: @CeRRF_Griffith l Figure 1. The DJI Inspire drone with a standard true-colour camera at the front, and multi-spectral and thermal cameras that have been attached by the user for giving the drone customised capability. l Figure 2. Normalised Difference Red Edge (NDRE) image of a rice crop showing effects of top-dressed nitrogen applied with a spreader. The non-uniform spreading pattern can be seen across the bays. l Figure 3. Late season rice crop NDVI taken from a drone, showing variable crop maturity (the darker red areas are more mature). Also, the effects of old contour layouts can be seen, particularly in the lower right of the image. Multi-Spectral True colour Thermal