1. FOR FURTHER INFORMATION Author names and affiliation: This information must always be placed above the ribbon below the title and subtitle. If more space is required, affiliations can be listed below the bottom ribbon where the references and acknowledgements are placed. Poster Title: Font size and line spacing can be altered in this section to suit the length of your title. Your title must be vertically aligned to the bottom. ARCAA, QUT, QLD GOV ?? Figure 1: CSIRO SMR-1 helicopter and 3D flight plan generated by CSIRO software for an autonomous survey in complex terrain. Poster content area: Images may bleed off to the right or left, do not place content over the ribbons. Keep within the content area indicated by the guides. Further information: Insert your contact details, including a business unit specific url. References & acknowledgements: Can appear below the bottom ribbon if you don’t have enough room on your poster. REFERENCES Merz T and Chapman SC (2011) Autonomous Unmanned Helicopter System for Remote Sensing Missions in Unknown Environments Proceedings of the International Conference on Unmanned Aerial Vehicle in Geomatics (UAV-g), Zurich, Switzerland, 14-16 September 2011. DOI: 10.1109/FG.2011.5771400 ACKNOWLEDGEMENTS Project ResQu was led by ARCAA in a collaboration withthe Queensland University of Technology (QUT), CSIRO, Boeing and Insitu Pacific with the support of the Queensland State Government Department of Science, Information Technology, Innovation and the Arts (DSITIA) UAVs in Agricultural and Ecological Research Breeding New Crop Varieties Background From 2009, CSIRO unmanned helicopters (see story at left) started to be used in plant breeding research in wheat, and more recently CSIRO began to modify cheap consumer multi-rotor copters to undertake shorter flights in sugarcane, cotton, sorghum and other crops. The Challenge New technologies have greatly increased our ability to genotype different plants to explore diversity and develop new varieties for farmers. The limitation is that we need to wait for crops until harvest to measure yield. UAVs and other imaging vehicles (see PhenoMobile display) allow us to measure other aspects of crop growth – ‘Phenotyping’– and to provide quantitative data from image analysis to breeders to accelerate selection. Our Response Plant breeding trials typically consist of 100s of plots of 5 to 20m 2 in size – not discernable by satellite. Over the last 5 years, CSIRO has internally funded development of software to capture and process images captured from normal, multi-spectral (5 band) and thermal (temperature) cameras. From this pipeline, the result is plot-level data on crop establishment, canopy cover, crop height, canopy ‘greenness’, canopy temperature (related to heat and drought stress), counts of ears/heads of grains and estimates of lodging. Unmanned aerial vehicles (UAVs) or Remotely Piloted Aircraft (RPAs), often termed drones, are being promoted for use in agriculture applications in multiple crops and ecosystems. While satellite technology may remain the best option in broad-acre cropping, small low-cost UAVs have an increasing role in the characterisation of research trials in breeding and agronomy research as well as in survey applications. Measuring and surveying plant species Scott Chapman, Torsten Merz, Bangyou Zheng, Jose Jimenez-Berni Protecting Rainforest from Weeds Background In the early 2000s, the only autonomous UAVs were military or customised systems that cost 100 to 500k. At that time, CSIRO robotics researchers developed and engineered a complete 'autopiloting' system for off-the-shelf remote control (RC) helicopters. Originally used for land and powerline surveys, these machines have been deployed into multiple applications. The Challenge Our rainforests are precious – less than two per cent of Australia's land area, confined to small patches clustered mostly in inaccessible, mountainous regions along the tropical coast. Unfortunately, a purple-leaved weed, Miconia calvescens, has invaded some areas of rainforest. Miconia is an unusually aggressive invader and has potential to cause irreversible damage to native plant and wildlife populations. AGRICULTURE AND FOOD / DATA61 Scott.Chapman escott.chapman@csiro.au whttp://people.csiro.au/C/S/Scott-Chapmanhttp://people.csiro.au/C/S/Scott-Chapman More Information Chapman SC, Merz T, Chan A, Jackway P, Hrbara S, Dreccer MF, Holland E, Zheng B, Ling TJ, Jimenez- Berni, J (2014) Pheno-copter: A low-altitude, autonomous, remote-sensing robotic helicopter for high- throughput field-based phenotyping. Agronomy 4, 279-301. http://www.mdpi.com/2073-4395/4/2/279 Colours: To change colours, go to the Design tab, click on the Colors button and select from the Custom list: CSIRO Blueberry, CSIRO Forest, CSIRO Midday (these colours), CSIRO Mint, CSIRO Sky or CSIRO Teal. Our Response Project ResQu was a two year, $7M project led by the Australian Research Centre for Aerospace Automation (ARCAA) in which CSIRO was a partner. Developed by robotics researchers at CSIRO in partnership with Biosecurity Queensland, the unmanned helicopters use sophisticated imaging technology for locating weeds. They offer a safer, more convenient way of mapping weeds in remote and difficult terrain (Figure 1). As the images and mapping data are all stored, it allows different departments to share data for comparison over time. More Information https://www.csiro.au/en/Research/BF/Areas/Protecting-Australias- agricultural-industries/Robot-technology/Project-ResQu https://confluence.csiro.au/display/AAR/Autonomous+Aircraft+Research http://caws.org.au/awc/2016/awc201611911.pdf 87% 65% 61% 68% 0730 0805 0920 1020 1100 1200 1415 1505 GEHEAT1 - 28 Sep 2012 Figure 2: Comparisons of wheat varieties for ground cover (top left), canopy temperature through the day (top right), and lodging before harvest (bottom right). Figure at bottom left shows a 3D reconstructed view of a breeding trial field. Equipment specification – camera lens/speed, aircraft flight specs Mission planningFlights Image collation and geo-reference Post-processing to generate mosaics and 3D Identification of trial images and plots Extraction of plot images, straightening and trimming Image spectral extraction and analysis Experiment analysis of plot-level data