Husna Nurarifah
Papers
1
Total Citations
8
H-Index
1
About
Husna Nurarifah is a researcher at the forefront of agricultural robotics, specializing in computer vision and precision automation for plant cultivation. Her work centers on developing intelligent systems that enable robots to perceive and interact with complex biological environments. Her most cited study, "Faster RCNN based leaf segmentation using stereo images" (2023, 8 citations), tackles a critical bottleneck in automated pruning: the accurate differentiation of leaves from branches. By leveraging aligned RGB-Depth images from a golden melon plant, she demonstrated how deep learning architectures like Faster R-CNN can achieve robust leaf segmentation in stereo imagery, a foundational step for robotic pruning. This contribution directly addresses the need for efficient, non-destructive plant manipulation in modern agriculture. Nurarifah’s research bridges computer vision and agri-tech, offering practical solutions for reducing labor dependency and improving crop yields. Her work is particularly notable for its integration of depth information to enhance segmentation accuracy in cluttered, natural scenes. As a rising voice in precision agriculture, she is helping to shape the future of autonomous farming systems.
Research Focus
Key Achievements
Top Papers
- 1Faster RCNN based leaf segmentation using stereo images8 citations · 2023