Muhammad Nur Aiman Shapiee
Papers
3
Total Citations
11
H-Index
2
About
Muhammad Nur Aiman Shapiee is a researcher at the forefront of applying deep learning to agricultural robotics and intelligent monitoring systems. His work centers on computer vision and transfer learning, with a primary focus on developing robust object detection models for precision agriculture. In his highly cited 2020 study, Shapiee pioneered the use of Convolutional Neural Network (CNN)-based detectors to classify chili plants and their leaves, a critical step for enabling robotic vision in crop monitoring and growth supervision. This foundational work, along with its 2022 follow-up, has garnered significant attention (5 and 4 citations respectively) for addressing the practical challenge of distinguishing crops from their environment. Expanding beyond agriculture, Shapiee has also contributed to human safety in collaborative robotics through deep learning-based human presence detection (2020, 2 citations), tackling the dimensionality limitations of conventional safety sensors. His research bridges the gap between advanced AI techniques and real-world applications, offering scalable solutions for both autonomous farming and human-robot interaction. Shapiee’s work is particularly valuable for students and engineers seeking to implement transfer learning in resource-constrained, real-time detection systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Deep Learning Based Human Presence Detection2 citations · 2020