Rasheed Khankan
Papers
1
Total Citations
7
H-Index
1
About
Rasheed Khankan is a rising researcher at the intersection of computer vision, machine learning, and agricultural technology, with a specific focus on smart mushroom cultivation. His most-cited work, “A novel dataset of annotated oyster mushroom images with environmental context for machine learning applications” (2024, 7 citations), provides a foundational resource for applying AI to mushroom farming. This dataset enables advances in yield prediction, growth analysis, disease and deformation detection, and digital twinning—critical challenges in the emerging smart mushroom industry. By bridging the gap between state-of-the-art computer vision and practical agricultural needs, Khankan’s contributions support the development of automated systems that improve efficiency and sustainability in mushroom production. His work is notable for its interdisciplinary approach, combining rigorous data annotation with environmental context to train robust machine learning models. As a researcher, Khankan is helping to revolutionize the agricultural sector, demonstrating how AI can transform traditional farming practices. His growing citation count reflects the immediate relevance and utility of his dataset for other researchers and practitioners in precision agriculture and applied machine learning.
Research Focus
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Top Papers
- 1