Romzi Izzudin Aufa
Papers
1
Total Citations
5
H-Index
1
About
Romzi Izzudin Aufa is a researcher at the forefront of applying deep learning to precision agriculture, with a primary focus on computer vision for fruit detection and counting. His work centers on optimizing convolutional neural network architectures to improve the accuracy and efficiency of automated agricultural monitoring systems. In his most cited study, Aufa conducted a comparative analysis of YOLOv7 variants for detecting and counting Batu 55 citrus fruits, demonstrating that the original YOLOv7 architecture significantly outperformed both the tiny and x versions in real-world orchard conditions. This research, which has garnered 5 citations since its 2024 publication, provides critical insights into model selection for agricultural robotics and yield estimation. By systematically evaluating single versus double label approaches, Aufa’s work addresses practical challenges in occluded and dense fruit environments, contributing to the development of more reliable smart farming technologies. His findings offer valuable guidance for researchers and engineers seeking to deploy deep learning solutions in agricultural settings, balancing computational efficiency with detection precision.
Research Focus
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Top Papers
- 1