Ahmad Tarmizi
Papers
1
Total Citations
60
H-Index
1
About
Ahmad Tarmizi is a leading researcher in the application of deep learning to aquaculture and marine biology, with a focus on underwater computer vision. His work addresses critical bottlenecks in fish farming, particularly the labor-intensive and error-prone process of fish detection and counting. Tarmizi’s most cited paper, "Underwater Fish Detection and Counting Using Mask Regional Convolutional Neural Network" (2022, 60 citations), introduces a novel approach that leverages Mask R-CNN to overcome the limitations of both traditional non-machine learning and earlier machine learning-based counting methods. This contribution provides a more accurate, automated solution for monitoring fish populations during hatching and production, directly supporting sustainable aquaculture development. His research bridges the gap between advanced neural network architectures and practical, real-world challenges in underwater environments. By pioneering these techniques, Tarmizi has established himself as a key innovator in precision aquaculture, with his work serving as a foundation for future studies in automated marine monitoring and resource management.
Research Focus
Key Achievements
Top Papers
- 1