Research output per year
Research output per year
Personal profile
Biographical Details
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 has gained a number of International and Australian awards including Australian Postgraduate Award, International Postgraduate Research Scholarship, Endeavour Research Fellowship, Outstanding graduate student award in 13th ICPA, Netherlands Fellowship Award.
Approved to Supervise
- Approved PhD/HDR supervisor
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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SDG 1 No Poverty -
SDG 2 Zero Hunger -
SDG 3 Good Health and Well-being -
SDG 9 Industry, Innovation, and Infrastructure -
SDG 12 Responsible Consumption and Production
Fingerprint
Dive into the research topics where Moshiur Rahman is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
- 1 Similar Profiles
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Improved yield forecasting of individual Sugarcane crops using an evolved remote sensing and Machine Learning approach
Rahman, M. M. & Robson, A. J., 3 Aug 2025, IGARSS 2025: International Geoscience and Remote Sensing Symposium3rd - 8th August, 2025. Australia, p. 3765-3769Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
1 Citation (Scopus) -
Machine Learning Approaches for Assessing Avocado Alternate Bearing Using Sentinel-2 and Climate Variables—A Case Study in Limpopo, South Africa
Rahman, M. M., Robson, A. & Bekker, T., 5 Dec 2025, In: Remote Sensing. 17, 24, p. 1-30 3935.Research output: Contribution to journal › Article › peer-review
Open Access -
Developing a reference method for indirect measurement of pasture evapotranspiration at sub-meter spatial resolution
Alam, M. S., Lamb, D. W., Rahman, M. M. & Randall, M., 31 Dec 2024, In: Smart Agricultural Technology. 9, p. 1-7 100567.Research output: Contribution to journal › Article › peer-review
Open Access -
Exploring the Relationship Between Very-High-Resolution Satellite Imagery Data and Fruit Count for Predicting Mango Yield at Multiple Scales
Torgbor, B. A., Sinha, P., Rahman, M. M., Robson, A., Brinkhoff, J. & Suarez, L. A., 8 Nov 2024, In: Remote Sensing. 16, 22, p. 1-23 4170.Research output: Contribution to journal › Article › peer-review
Open Access6 Citations (Scopus) -
Integrating Remote Sensing and Weather Variables for Mango Yield Prediction Using a Machine Learning Approach
Torgbor, B. A., Rahman, M. M., Brinkhoff, J., Sinha, P. & Robson, A., 2 Jun 2023, In: Remote Sensing. 15, 12, p. 1-26 3075.Research output: Contribution to journal › Article › peer-review
Open Access36 Citations (Scopus)
Projects
- 12 Finished
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Pilot project to automate the derivation and delivery of time series sugar cane yield models at the individual block level for 100 sugar can blocks. Also provide annual regional yield forecasts and Condong, Broadwater and Harwood mill areas for 2024 harvest season.
Robson, A. (UNE Lead Investigator), Rahman, M. (Chief Investigator) & Clark, S. (Other)
10/07/24 → 1/04/25
Project: Grant
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Develop ‘time series’ yield forecasting models and block level yield forecasts for 436 New Zealand avocado orchard blocks
Robson, A. (UNE Lead Investigator), Clark, S. (Other) & Rahman, M. (Chief Investigator)
7/05/24 → 30/11/24
Project: Grant
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Develop avocado yield forecasting ‘time series’ models for New Zealand farms Largus, Mapua and Tiri and provide block and level yield forecasts for the 2024 harvest season
Rahman, M. (UNE Lead Investigator), Clark, S. (Other) & Robson, A. (Chief Investigator)
18/03/24 → 1/09/24
Project: Grant
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2024 Sugarcane Yield Forecasting for Tully Sugar
Rahman, M. (UNE Lead Investigator), Robson, A. (Co-Investigator) & Clark, S. (Other)
5/03/24 → 10/12/24
Project: Grant
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'Time series’ yield forecasting of sugar cane grown under African growing conditions
Rahman, M. (UNE Lead Investigator), Robson, A. (Co-Investigator) & Clark, S. (Other)
31/08/23 → 31/12/23
Project: Grant