Rusab Sarmun
Papers
1
Total Citations
10
H-Index
1
About
Rusab Sarmun is a rising researcher at the intersection of computer vision and agricultural technology, with a primary focus on deep learning applications 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), represents a significant advancement in automated crop management. Unlike prior studies that relied on limited datasets and binary ripe/unripe classification, Sarmun's research leverages the state-of-the-art YOLOv8 architecture to achieve real-time, multi-stage ripeness detection on low-cost edge devices like the Raspberry Pi. This work not only demonstrates the feasibility of deploying sophisticated neural networks in resource-constrained agricultural settings but also provides a practical, scalable solution for farmers to optimize harvesting schedules. With 10 citations in its first year, the paper is already influencing the field of smart agriculture. Sarmun's contributions exemplify how cutting-edge computer vision can be democratized for real-world agricultural challenges, bridging the gap between advanced AI research and accessible farming technology.
Research Focus
Key Achievements
Top Papers
- 1