Md. Rajibul Islam

Papers

Total Citations

H-Index

About

Md. Rajibul Islam 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 impactful work, "Revolutionizing Rose Grading: Real-Time Detection and Accurate Assessment with YOLOv8 and Deep Learning Models" (2024), demonstrates a novel approach to automating flower quality assessment using state-of-the-art object detection. By integrating YOLOv8 with custom deep learning architectures, Islam achieved real-time, high-accuracy grading that significantly outperforms traditional manual methods. This contribution has already garnered 6 citations, signaling strong early interest from the agri-tech and computer vision communities. His research addresses a critical bottleneck in floriculture supply chains, where rapid, objective quality control can reduce waste and increase profitability. Islam’s work exemplifies how modern AI can be tailored for niche agricultural tasks, offering scalable solutions for developing economies. As an emerging scholar, his focus on real-time detection and assessment positions him as a promising voice in the growing field of smart agriculture, with potential for broader applications in fruit and vegetable grading.