Skip To Main Content
raihan islam on Muck Rack

raihan islam

As seen in: Nature, arXiv, Computer Weekly, The Virginian-Pilot, Daily Press (Newport News, VA), Augusta Free Press, Our Community Now

Get in touch with raihan

Contact raihan, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack

Actions

Share this page

Articles

Annotation-efficient weed detection using DINOv3-distilled YOLOv12

|
By saif khan, Raiyan Gani, Mahamudul Hasan, raihan islam, Osama Bin Qashem, Mahmudul Hasan Hamim, Nusrat Jahan Oishi
| Nature Verified
Abstract Weed pressure causes global crop yield losses of 10–34%, while the deployment of deep learning-based weed detection systems at scale remains constrained by the high cost of bounding-box annotation across diverse field environments.
Open in Who Shared Issue with article?

A lightweight graph-enhanced deep learning framework for explainable cucumber leaf disease diagnosis

|
By raihan islam, Ahmed Reza, Shamim H. Ripon, Raiyan Gani, Maherun Nessa Isty, Yusuf Salehin
| Nature Verified
Abstract Accurate and efficient identification of cucumber leaf diseases is a critical step in preventing losses and facilitating timely intervention in agricultural activi-ties. However, most state-of-the-art plant disease recognition models, including those employing deep learning, often fail to identify spatial dependencies among symptomatic leaf feature regions, require high computational resources, and lack robustness in their predictions.
Open in Who Shared Issue with article?

Benchmarking hybrid CNN and transformer backbones with graph convolution networks (GCN) for flower growth-stage classification

|
By Aritra Das, raihan islam, Ahmed Reza, Karib Shams, Shamim H. Ripon
| Nature Verified
Abstract Accurate recognition of flower growth stages is important for plant phenotyping but remains challenging due to subtle visual differences and limited labeled data. This study proposes a hybrid CNN/Transformer + GCN framework for fine-grained flower growth-stage classification. A new dataset, BD Flower Growth, is introduced with 3,889 original images from eight Bangladeshi flower species, categorized into three stages (early, mid, full), forming 24 classes.
Open in Who Shared Issue with article?
See all 16 articles