Rizky Mulya Sampurno
University of Tsukuba, Tsukuba University of Technology, Padjadjaran University
Papers
6
Total Citations
174
H-Index
4
About
Rizky Mulya Sampurno is a leading researcher in agricultural robotics, specializing in computer vision, deep learning, and automation for orchard management. His work centers on developing intelligent systems for fruit harvesting, weed detection, and collision-free navigation in complex orchard environments. Sampurno’s major contributions include the Faster-YOLO-AP algorithm, a lightweight apple detection model that achieved 113 citations for its efficient PDWConv architecture, significantly improving real-time fruit recognition. He also pioneered intrarow uncut weed detection using YOLO instance segmentation, addressing the labor-intensive task of mechanical weed management within orchard rows. His research on 3D camera and LiDAR integration for apple localization and 6-DoF manipulator path planning using Bi-RRT algorithms has advanced autonomous harvesting, with over 170 total citations across his top papers. Notable achievements include developing a low-cost robotic weeder and a dual-view fruit localization method to overcome occlusion challenges. Sampurno’s work is pivotal in reducing manual labor in agriculture, offering scalable, AI-driven solutions for precision farming. His innovations continue to shape the future of orchard automation, making him a key figure in the field.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6