Munshi Sajidul Islam
Papers
1
Total Citations
10
H-Index
1
About
Munshi Sajidul Islam is a rising researcher at the forefront of applying deep learning to precision agriculture, with a particular focus on real-time crop monitoring and automation. His most cited work, "Deep learning-based real-time detection and classification of tomato ripeness stages using YOLOv8 on Raspberry Pi" (2025, 10 citations), addresses a critical gap in agricultural technology by moving beyond binary ripe/unripe classification to multi-stage ripeness detection. By deploying the state-of-the-art YOLOv8 model on low-cost, edge-computing hardware like the Raspberry Pi, Islam demonstrates how advanced computer vision can be made accessible for practical, on-field use—enabling farmers to automate harvesting decisions with unprecedented granularity. His approach significantly expands upon earlier studies limited by small datasets and simplistic classification, offering a scalable solution that balances accuracy with real-time performance. This work not only showcases his technical expertise in deep learning and embedded systems but also underscores his commitment to solving real-world agricultural challenges. As his citation count grows, Islam is establishing himself as a key contributor to the intersection of AI and sustainable farming, with potential applications extending to other crops and automated quality assessment systems.
Research Focus
Key Achievements
Top Papers
- 1