Mohammed Ayoub Juman

Mohammed Ayoub Juman is a Lecturer in the Department of Mechatronics at Monash University Malaysia's School of Engineering. His research focuses on integrating automation into agricultural systems, particularly oil palm plantations, using machine vision, robotics, and neural networks. He aims to address labor shortages and enhance productivity through smart technologies.

His expertise includes Agricultural Robotics, Machine Learning, and Industrial Automation. Key projects include 'Agriculture 4.0 - Investigation on Fusing Digital Twins and Deep Learning for improving Agricultural Yield and Autonomy' (2023-2025) and 'Research A Novel Incremental Learning with PCA Convolutional Neural Network (ILPCNN) for Fine-Grained Vehicle Model Recognition' (2021-2025). These projects highlight his work in autonomous systems, sensor fusion, and deep learning applications.

His research aligns with UN Sustainable Development Goals, particularly in improving food systems and sustainable agriculture. Notable outputs include studies on energy load forecasting, flood vulnerability assessment, and cardiovascular disease detection through AI-driven methods.

Collaborators include researchers from Monash University Malaysia and external institutions, emphasizing cross-disciplinary work in robotics, biomedical engineering, and environmental science.