Md. Ahasan Atick Faisal
Papers
1
Total Citations
10
H-Index
1
About
Md. Ahasan Atick Faisal is a rising researcher at the intersection of computer vision and agricultural technology, with a primary focus on deep learning-based object detection for precision farming. 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 significant advancement over prior binary classification approaches by enabling multi-stage ripeness detection on resource-constrained edge devices. This contribution addresses a critical gap in automated crop management, offering a scalable, real-time solution that can be deployed directly in the field. By leveraging the YOLOv8 architecture on a Raspberry Pi platform, Faisal demonstrates how state-of-the-art AI can be made accessible for practical agricultural applications, reducing hardware costs while maintaining high accuracy. His work is notable for its emphasis on real-world deployment, bridging the gap between laboratory research and on-farm implementation. As a researcher, Faisal is establishing himself in the growing field of AI-driven agriculture, with potential to influence smart farming practices and automated harvesting systems.
Research Focus
Key Achievements
Top Papers
- 1