Innovative AI Technologies Driving Sustainable Farming: Strategies for Improving Food Security

Professor Aboul Ella Hassanien is a Professor of Information Technology at Cairo University and the Founder and Chair of the Scientific Research School of Egypt (SRSEG), formerly known as SRGE. He earned his B.Sc. and M.Sc. from Ain Shams University and his Ph.D. in Computer Science from Tokyo Institute of Technology in 1998. He has authored over 1000 research papers and 45+ books in areas including data mining, intelligent systems, medical image analysis, and smart environments. His work also covers computational intelligence, security, and multimedia data mining.Professor Ashraf Darwish is a Professor of Computer Science at Helwan University, Egypt, and earned his Ph.D. from Saint Petersburg State University in 2006. He is Vice Chair of the Scientific Research School in Egypt (SRSEG), focusing on Computer Science and IT. His research centers on Artificial Intelligence and its applications, including machine learning, deep learning, computer vision, Digital Twins, the Metaverse, Explainable AI, and Generative AI. Professor Darwish serves as Editor-in-Chief and Associate Editor for several international journals and is a senior member of IEEE. He has authored numerous publications in top-tier journals and conferences. From 2011 to 2015, he served as Egypt’s Cultural and Educational Chancellor to Kazakhstan and Central Asia. He received Helwan University’s Best Researcher Award in 2014 and is listed among the top 2% of most-cited scientists globally, according to Stanford University’s Ioannidis Index. Currently, he is Chancellor of the Egyptian Chinese University in Cairo and a member of the Council of Communication Research and IT at ASRT, Ministry of Higher Education and Scientific Research.

• Section I: AI and Deep Learning for Disease Detection and Plant Health.- Section II: Optimization and Deep Learning for Yield Prediction and Crop Management.- Section III: AI and Deep Learning for Fruit and Vegetable Classification.- Section IV: Generative and Multi-Label Learning for Food Analysis.