Rate My Professor Ayoub Juman

Dr. Mohammed Ayoub Juman serves as a Lecturer in the Mechatronics Discipline within the School of Engineering at Monash University Malaysia. He holds a Doctor of Philosophy in Electrical and Electronic Engineering from the University of Nottingham Malaysia, awarded in 2019, where his doctoral research focused on creating a navigation system for an autonomous mobile robot designed to operate in oil palm plantations to introduce automation into agricultural areas. He also earned a Master in Mechatronics Engineering (Honours) from the University of Nottingham Malaysia in 2014. Juman joined Monash University Malaysia in 2016 and has since contributed to teaching courses such as Dynamical Systems, Mechatronics and Manufacturing, and Final Year Projects in robotics and mechatronics. His academic career emphasizes practical applications of engineering in real-world challenges, particularly in agriculture.

Juman's research specializations include agricultural robotics, automation, machine vision, machine learning, and neural networks, with a primary focus on oil palm plantations as a continuation of his doctoral work. His research aims to improve standards of living through smart systems, addressing labor shortages in industries by enhancing productivity without replacing workforces. This involves better crop growth, higher yields, and improved plantation management using machines, sensors, and data processing techniques. Key publications feature 'A novel tree trunk detection method for oil-palm plantation navigation' (Juman et al., 2016, Computers and Electronics in Agriculture), 'An integrated path planning system for a robot designed for oil palm plantations' (Juman et al., 2017, IEEE TENCON), 'An incremental unsupervised learning based trajectory controller for a 4 wheeled skid steer mobile robot' (Juman et al., 2019, Engineering Applications of Artificial Intelligence), 'A regression unsupervised incremental learning algorithm for solar irradiance prediction' (Puah et al., 2021, Renewable Energy), 'Attention Mechanisms in Deep Learning Models for Short-Term Energy Load Forecasting' (Sendanayake et al., 2023, IEEE SCOReD), 'A Cyber-Physical Precision Agriculture System for Plant Growth and Yield Prediction' (Ng et al., 2024, IEEE AGRETA), 'GripDepthSense3DNet: A Depth-Enabled Hardness Sensing Framework in Soft Robotic Grasping' (Ling et al., 2025, Soft Robotics), and 'An integrated convolutional neural network with zero-dimensional cardiovascular hemodynamics parameters for early cardiovascular disease detection' (Sooriamoorthy et al., 2025, Biomedical Signal Processing and Control). His methodology integrates machine vision and learning techniques with mobile robots and sensors for indoor and outdoor applications, especially in agriculture.