Dr Greg Falzon
Senior Lecturer in Computational Science - School of Science and Technology; Precision Agriculture Research Group (PARG)
Phone: +61 +61 2 6773 2387
My PhD was a multi-disciplinary project involving medical physics and statistical image processing. More specifically I investigated the supra-molecular structure of breast cancer tissue using Small-Angle X-ray Scattering. I developed algorithms which utilised the contourlet transform and machine learning to both accurate diagnose malignant pathologies as well as provide a physical understanding of the factors which lead to this accurate identification.
PhD (Biomedical Image Analysis) UNE, BSc(Hons I), DipEd
IEEE, IEEE Geoscience & Remote Sensing Society, IEEE Signal Processing Society, IEEE Computational Intelligence Society, IEEE Engineering in Medicine & Biology Society.
I have consulted on projects for the Biosecurity, Livestock and Sugar industries.
- Computational Science (Big Data and Data Science)
- Decision Support Systems
- Geo-statistical Modelling
- Information Technology Projects
- Internet Programming
- Internet Security
- Multi-Level Models
- Special Research Topics (Data Mining, Real-time Signal Processing).
I have a broad range of interests in data science, computational modelling and computer vision, particularly embedded vision. That is making small smart devices that can recognise objects visually. I am also deeply committed to translating the benefits of this research to the agricultural, ecological and health sectors.
Major projects at present include:
The impact of wild dogs on the livestock industry in this country can be absolutely devastating. Negative impacts are felt across the board whether it be from an economic, mental health, stock welfare or wildlife perspective. As a member of a rural community I have seen firsthand the devastation that can occur. Researching in to how to utilise technology and data analysis techniques to improve wild dog management is a key strategic research area of mine. Specific projects include Wild Dog Alert an automated computer system to recognise wild dogs using camera traps and Electronic Shepherd a computer system designed to monitor stock welfare and detect dog attacks on stock.
The future of the internet promises smart devices which can communicate to each other and which can monitor the environment autonomously. This research projects explores the possibilities that this technology could offer farmers. Sensor node stations are placed in situ in paddocks to monitor conditions such as soil moisture content and temperature. This data is relayed wirelessly and in real-time to a central on farm computer on which very advanced geo-spatial algorithms are used to process the data across the entire farm and advise the farmer of conditions such as the optimal time to sow a crop and provide advanced warning of drought.
The threat of a biosecurity incursion into our country poses a significant risk to our primary industries (agriculture, fisheries and forestry), biodiversity and public health. A key component in protecting Australia is adequate surveillance and early detection of threats before they establish. This research program investigates the development of intelligent systems and big data analytics to enhance our capabilities in this area.
Falzon G, Meek P, & Vernes K (2014) ‘Computer-assisted identification of small Australian mammals in camera trap imagery’ In ‘Camera Trapping in Wildlife Management and Research’ (Eds P. Meek, P. Fleming, G. Ballard, P. Banks, A. Claridge, J. Sanderson and D. Swann). (CSIRO Publishing: Melbourne)
Meek PD, Ballard GA, Fleming PJS, Schaefer M, Williams W, & Falzon G. ‘Camera traps can be seen and heard by animals’. PLoS ONE, 9(10):e110832.
Cosby A, Falzon G, Trotter M, Stanley J, Powell K, & Lamb DW ‘Risk mapping of redheaded cockchafer (Adoryphoruscouloni) (Burmeister) infestations using a combination of novel k-means clustering and on-the- go plant and soil sensing technologies’, accepted Precision Agriculture March 27 2014.
Stanley J, Lamb D, Falzon G, & Schneider D (2014) ‘Apparent electrical conductivity (ECa) as a surrogate for neutron probe counts to measure soil moisture content in heavy clay soils (Vertosols)’, Soil Research, 52(4): 373-378.
Meek PD, Vernes K, & Falzon G (2013) ‘On the Reliability of Expert Identification of Small-Medium Sized Mammals from Camera Trap Photos, Wildlife Biology in Practice 9(2):1-19.
Falzon G, Lamb DW & Schneider D ‘The Dynamic Aerial Survey Algorithm Architecture And Its Potential Use In Airborne Fertilizer Applications’, (2013) IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5(6):1772-1779.
Taylor K, Griffith C, Lefort L, Gaire R, Compton M, Wark T, Lamb D, Falzon G, Trotter M (2013) ‘Farming the Web of Things, IEEE Intelligent Systems, 28(6): 12-19.
Falzon G, Schneider D, Trotter M, & Lamb DW (2013) ‘A relationship between faecal egg counts and the distance travelled by sheep’, Small Ruminant Research, 111(1-3):171-174.
Research Supervision Experience
Doctor of Philosophy
H. Alharbi 'Automatic Removal of Pectoral Muscle from Mammogram Images for Robust Mass Detection'.
A. Cosby 'Developing a landscape risk assessment for the redheaded cockchafer (Adoryphorus couloni) in dairy pastures using precision agriculture sensors'.
P. Meek 'Analysis of the functionality, value and constraints of using camera traps for wildlife monitoring and ecological research'.
J. Roberts 'Potential for Remote Monitoring of Cattle Movement to Indicate Available Biomass'.
E. Sadgrove 'Detection of Flora and Fauna in Pastoral Landscapes using Unmanned Aerial Vehicles'.
P. Zada 'A vulnerability analysis on the adoption of mobile internet e-voting in Australia'.
ABC Radio National The Science Show with Robyn Williams:Maths behind breast cancer detection, bettong tracking, and application of fertiliser.
ABC NSW Country Hour, Sound technology joins the fight against wild dogs
Stock Journal, Livestock, Gotcha! Traps snap suspects
The Land, Wild dog war calls in military skills
NBN News, Voting Bought into 21st Century
J.B. Douglas Award for outstanding postgraduate research, Statistical Society of Australia Inc. New South Wales Branch (2006).
Science and Innovation Awards for Young People in Agriculture, Fisheries and Forestry: Australian Wool Innovation Division (2015).