Charles Sturt University
Charles Sturt University

Dr D M Motiur Rahaman

Dr D M Motiur Rahaman

Dr Motiur Rahaman received the PhD degree in Information and Communication Technology (ICT) from Charles Sturt University, Australia in 2019. He is currently working as a postdoctoral research fellow in the National Wine and Grape Industry Centre, Charles Sturt University, Australia. Before that, he was working as a research associate-Image Processing & Machine Learning (04 October 2018-03 March 2019) in the National Wine and Grape Industry Centre, Charles Sturt University, Australia.

Dr Motiur was a Lecturer in the Department of Electrical, Electronics and Telecommunication Engineering, Dhaka International University, Bangladesh from 15 September 2011 to 31 January 2015. He was a general workshop Chair- the 9th Pacific-Rim Symposium on Image and Video Technology (PSIVT) 2019, hosted by Charles Sturt University, Sydney, Australia, and 18-22 November 2019.

His research interest resides in the field of artificial intelligence, machine learning, deep learning, computer vision, image processing, video coding, emotion detection and big data analysis. He has authored several refereed papers/articles in these fields. Dr Motiur were invited speakers in IEEE Conference on Image and Vision Computing New Zealand (IVCNZ 2015), University of Canterbury, Christchurch, New Zealand, IEEE International Conference on Digital Image Computing: Techniques and Applications (2016, 2017 and 2018) and IEEE International Conference on Multimedia and Expo (ICME 2016), Seattle, USA.

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Specialisation: Image Processing, Machine Learning and Deep Learning

Focus area: To provide a user-friendly App for quick assessments of vine nutrient deficiency symptoms in the field.

Research Undertaken (summary): Development of an App that provides information to growers on nutritional disorders in a white and red variety. The diagnostic tool allows vineyard managers and growers to take a photo of a leaf with a smartphone and using customized Machine Learning (ML) and computer vision techniques give an assessment for deficiency. The App provides location information through the GPS co-ordinates the grower will be able to track seasonal and even longer term data on deficiency changes. The App will be tested by growers to solicit feedback on the appropriateness of the user interface and usefulness/applicability of the app to their situation.

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