Lanhui Fu
Papers
2
Total Citations
154
H-Index
2
About
Lanhui Fu is a prominent researcher specializing in agricultural computer vision, precision agriculture, and deep learning applications for robotic harvesting systems. With a focused body of work centered on automated crop detection in natural environments, Fu has made significant strides in bridging the gap between artificial intelligence and practical agricultural robotics. Fu's most recognized contributions lie in the detection and recognition of banana crops under real-world orchard conditions — notoriously challenging environments characterized by complex lighting, occlusion, and natural variability. Their 2019 paper on banana detection using color and texture features garnered 82 citations, establishing a foundational methodology for vision-based crop recognition. Building on this, Fu developed YOLO-Banana, a lightweight neural network tailored for the rapid, real-time detection of banana bunches and stalks, which has accumulated 72 citations since its 2022 publication and reflects the growing demand for computationally efficient deep learning models deployable on agricultural robots. Together, these works represent a coherent and impactful research trajectory, with over 150 combined citations underscoring Fu's influence in the field. For students and researchers exploring precision agriculture and autonomous harvesting technologies, Fu's work offers both practical algorithms and methodological inspiration.
Research Focus
Key Achievements
Top Papers
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