Tawsifur Rahman
Papers
1
Total Citations
10
H-Index
1
About
Tawsifur Rahman is a rising figure in applied deep learning and agricultural automation, with a focus on real-time computer vision systems for crop management. His most-cited work, “Deep learning-based real-time detection and classification of tomato ripeness stages using YOLOv8 on Raspberry Pi” (2025, 10 citations), introduces a novel approach that moves beyond traditional binary ripe/unripe classification by enabling multi-stage ripeness detection directly on low-cost edge devices. This contribution is significant for its practical deployment potential in precision agriculture, reducing hardware costs while maintaining high accuracy. Rahman’s research bridges state-of-the-art object detection architectures—like YOLOv8—with embedded systems, making AI-driven harvesting and monitoring more accessible to small-scale farmers. His work has already garnered attention for its scalability and real-world applicability, marking him as an innovator at the intersection of deep learning and sustainable agriculture. As his citation count grows, Rahman continues to push the boundaries of efficient, deployable AI solutions for food production challenges.
Research Focus
Key Achievements
Top Papers
- 1