Keynote Speakers

1)  Prof. Arun Kumar Sangaiah (Yushan Young Scholar, Highly Cited Researcher)

Distinguised Professor, International Grauduate School of AI

National Yunlin University of Science and Technology, Taiwan (ROC); VIT University, India


Bio:

Prof. Arun Kumar Sangaiah received his Ph.D. from School of Computer Science and Engineering, VIT University, Vellore, India. He is currently a Full Professor with National Yunlin University of Science and Technology, Taiwan. He has published more than 200 research articles in refereed journals (IEEE TII, IEEE TITS, IEEE TNSE, IEEE TETCI, IEEE SysJ, IEEE SensJ, IEEE IOTJ, ACM TOSN) 11 edited books, as well as 1 patents (held and filed) and  3 projects, among two of them funded by National Science and Technology Council (NSTC), Taiwan, Ministry of IT of India and few international projects (CAS, Guangdong Research fund, Australian Research Council) cost worth of 500000 USD. Dr. Sangaiah has received many awards, Yushan Young Scholar, Clarivate Highly Cited Researcher (2021,2022, 2023), Top 2% Scientist (Standord Report-2020,2021,2022), PIFI-CAS fellowship, Top-10 outstanding researcher, CSI significant Contributor etc. Also, he is responsible for Editor-in-Chief, and Associate Editor of various reputed ISI journals. Dr. Sangaiah is a visiting scientist (2018-2019) with Chinese Academy of Sciences (CAS), China and visiting researcher of Université Paris-Est (UPEC), France (2019-2020) and etc. His Google Scholar Citations reached 23000+ with h-index: 82 and i10-index: 298.


Talk Abstract

Edge IoT and Cognitive Intelligence System in UAV On-Board Intelligence for Sustainable Agriculture


The talk aims to discuss a cognitive system with a decisive deep learning (DL) architecture to investigate the use of vision-guided UAVs (Unmanned Aerial Vehicles) for precision agriculture. The main goals are to realize the automatic sky farming by UAVs, which means that the UAVs will do all the agronomy tasks automatically. The major points to be covered in the keynote are:

1) Need of a vision-guided localization and vision navigation approach which uses camera and other sensors on board and on field to realize the UAVs‘ controlling and Navigation method that doesn´t rely on the GPS (Global Positioning System) signal. 

2) Significance of a deep learning algorithm for crop disease detection. The main intention is to use DL/machine learning techniques to create a mapping between places obtained using cameras and actual on-field locations obtained via field sensors to increase the accuracy of recognized locations.