Dr Muhammad Moshiur Rahman
Research Fellow - Precision Agriculture Research Group (PARG)
Phone: +61 6773 1491
Dr Moshiur Rahman is a research scientist with more than 10 years’ experience in precision agricultural technologies. He has been involved in a number of industry funded projects, where he has developed prediction models for different agricultural and horticultural crops. His research fields include Satellite Image Processing, Machine Learning, Pattern recognition, Computer Modelling, and their applications to address relevant industry problems such as crop growth monitoring, yield prediction and overall improved adoption of precision agricultural technologies. His main expertise resides in the use of satellite remote sensors, such as WorldView (WV), SPOT, Sentinel etc.; and proximal sensors including CropCircle, Quantum Bar Sensor, Radiometer, Time domain Reflectometry (TDR) etc. to develop crop-climatic models for agricultural industry. He is an advanced user of Python and R code. He gained a number of International and Australian award including Australian Postgraduate Award, International Postgraduate Research Scholarship, Endeavour Research Fellowship, Outstanding graduate student award in 13th ICPA, Netherlands Fellowship Award.
BSc (Agricultural Engineering and Technology), BAU, Bangladesh
MSc (Agriculture and Bioresource Engineering), Wageningen University, The Netherlands
PhD (Precision Agriculture), University of New England, Australia
- Presidents Medal at ASSCT 2016 Conference
- Endeavour Research Fellowship
- Outstanding Graduate Student Award in 12th ICPA
- International Postgraduate Research Scholoarship
- Australian Postgraduate Award
- Nuffic Scholarship
Precision Agriculture (PA335)
Web Programming (COSC260)
Sustaining Rural Environment (RSNR120)
Primary Research Area/sCrop Climate Modelling; Remote Sensing for Agricultural Crops; Crop Water Use Efficiency
- Co-Project Leader – Implementing Precision Agriculture Solutions in Australian Avocado Production Systems (AV-18002)
- Co-Project Developer and Researcher – Multi-scale Monitoring Tools for Managing Australian Tree Crops – Industry Meets Innovation (ST15002)
- Co-Project Developer and Researcher – Sugar from Space: Improved Data Access, Yield Forecasting and Targeted Nitrogen Application for the Australian Sugar Industry
Research Supervision Experience
Yes, one PhD student.
Rahman, M. M., Robson, A., & Bristow, M. (2018). Exploring the Potential of High Resolution WorldView-3 Imagery for Estimating Yield of Mango. Remote Sensing, 10(12), 1866.
Rahman, M. M., Lamb, D. W., & Samborski, S. M. (2019). Reducing the influence of solar illumination angle when using active optical sensor derived NDVIAOS to infer fAPAR for spring wheat (Triticum aestivum L.). Computers and Electronics in Agriculture, 156, 1-9.
Alam, M. S., Lamb, D. W., & Rahman, M. M. (2019). In-situ partitioning of evaporation and transpiration components using a portable evapotranspiration dome—A case study in Tall Fescue (Festuca arundinacea). Agricultural Water Management, 213, 352-357.
Anderson, N. T., Underwood, J. P., Rahman, M. M., Robson, A., & Walsh, K. B. (2018). Estimation of fruit load in mango orchards: tree sampling considerations and use of machine vision and satellite imagery. Precision Agriculture, 1-17.
Alam, M. S., Lamb, D. W., & Rahman, M. M. (2018). A refined method for rapidly determining the relationship between canopy NDVI and the pasture evapotranspiration coefficient. Computers and electronics in agriculture, 147(C), 12-17.
Robson, A., Rahman, M. M., & Muir, J. (2017). Using Worldview Satellite Imagery to Map Yield in Avocado (Persea americana): A Case Study in Bundaberg, Australia. Remote Sensing, 9(12), 1223.
Rahman, M. M., & Lamb, D. W. (2017). The role of directional LAI in determining the fAPAR–NDVI relationship when using active optical sensors in tall fescue (Festuca arundinacea) pasture. International journal of remote sensing, 38(11), 3219-3235.
Yes, interested in relevant discipline
International Society of Precision Agriculture