Kittiphum Pawikhum
Papers
2
Total Citations
8
H-Index
2
About
Kittiphum Pawikhum is a researcher at the forefront of precision agriculture, specializing in the intersection of computer vision and robotics for sustainable orchard management. His primary research focuses on developing automated solutions for apple crop load management, a critical process that determines fruit quality. Pawikhum’s major contributions include the creation of a machine vision system that leverages a YOLOv8-based deep learning model for accurate apple bud detection in complex orchard environments, alongside a validated semi-automated method for branch diameter measurement. This work, detailed in his most cited paper (2025, 5 citations), directly addresses the labor-intensive challenge of precision thinning. He has also advanced the field through the design of specialized end-effectors for robotic thinning at the green fruit stage (2023, 3 citations), offering a promising alternative to traditional hand and chemical methods. By integrating real-time sensing with robotic actuation, Pawikhum’s research lays the groundwork for fully autonomous, data-driven crop load management, promising to enhance both fruit quality and operational efficiency in modern apple orchards.
Research Focus
Key Achievements
Top Papers
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