Md. Nahiduzzaman
Papers
1
Total Citations
10
H-Index
1
About
Md. Nahiduzzaman is a rising researcher at the forefront of applying deep learning to agricultural automation and computer vision. His work centers on developing real-time, edge-computing solutions for crop monitoring and post-harvest quality assessment, with a particular focus on fruit ripeness detection. His most cited paper, "Deep learning-based real-time detection and classification of tomato ripeness stages using YOLOv8 on Raspberry Pi" (2025, 10 citations), represents a significant leap forward: it moves beyond traditional binary ripe/unripe classification to enable multi-stage ripeness detection using the state-of-the-art YOLOv8 architecture, all deployed on a low-cost Raspberry Pi platform. This work demonstrates how advanced neural networks can be made accessible for practical agricultural applications, reducing hardware costs while maintaining high accuracy. Nahiduzzaman's contributions are helping bridge the gap between cutting-edge AI and on-field deployment, making precision agriculture more feasible for small-scale farmers. His research has already garnered attention for its innovative integration of lightweight models with embedded systems, and he continues to push boundaries in real-time object detection for agricultural robotics and smart farming.
Research Focus
Key Achievements
Top Papers
- 1