PhD Candidate presents research at prestigious IFAC AGRICONTROL 2019 Conference

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Syed Muhammad Abbass (Ph.D. Candidate) along with his advisor Dr. Abubakr Muhammad, Executive Director WIT, attended and presented their research at the 6th International Federation of Automatic Control (IFAC) on Sensing, Control and Automation Technologies for Agriculture – AGRICONTROL 2019, in Sydney Australia from 4th-6th December 2019. It is a peer-reviewed conference, hosted by the University of New South Wales (UNSW) Sydney this year and the objective of this conference is to promote and disseminate sensing, control and automation research directed at agriculture. The conference scope was spread over areas of fundamental, developmental, as well as applied and experimental work from the researchers working on the broader area of agricultural automation and was attended by the leading researchers in this domain from around the globe.

Abbas presented the research, titled, “Autonomous Canal Following by a Micro-Aerial Vehicle Using Deep CNN” on Thursday, 5th December 2019. The motivation for the research paper was to propose an autonomous navigation system for a Micro Aerial Vehicle to traverse the length of a water canal without any human intervention. Canal traversal is an important and periodic operation performed frequently to estimate the canal bank erosion, silt accumulation and structural damages indicted over time which significantly reduces the water carrying capacity of water channels. In his paper, he proposed an aerial autonomous canal traversal system using ResNet50 inspired deep convolutional neural network (CNN). Given the uniqueness of the problem, a dataset was generated for supervised learning and validation, and later evaluated the proposed approach on a real canal. This approach was implemented on a COTS micro-aerial vehicle. The system was designed in such a way that it takes 200ms from perception to action thereby making the system real-time. The superior performance of customized ResNet50 inspired network was also compared with other state-of-the-art CNNs trained on the canal datasets. Hashim Ali, a former MS student from the Electrical Engineering department is also a co-author in this research.

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