Amirul Asyraf Abdul Manan
Papers
2
Total Citations
9
H-Index
2
About
Amirul Asyraf Abdul Manan is a researcher focused on agricultural technology and computer vision, with a particular emphasis on applying deep learning to plant monitoring and classification. His work centers on the development of intelligent systems for precision agriculture, specifically using convolutional neural networks (CNNs) and transfer learning to detect and classify chili plants and their leaves. His most-cited papers, including "Chili Plant Classification using Transfer Learning models through Object Detection" (2020, 5 citations) and a 2022 follow-up (4 citations), demonstrate his contributions to robotic vision for agriculture. These studies are foundational for automating plant growth supervision, enabling more efficient monitoring and yield prediction. By leveraging object detection models, Manan’s research bridges the gap between computer vision and real-world agricultural challenges, offering practical solutions for farmers and researchers. His work has been cited in related fields, highlighting its relevance to sustainable farming and smart agriculture. Manan’s achievements underscore his role in advancing AI-driven tools that enhance crop management, making him a notable contributor to the intersection of machine learning and agricultural science.
Research Focus
Key Achievements
Top Papers
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