Pengyu Chu
Papers
12
Total Citations
505
H-Index
9
About
Pengyu Chu is a leading researcher in agricultural robotics and computer vision, with a specialized focus on automating fruit harvesting to address the pressing challenges of labor shortages and rising costs in the apple industry. His work spans the full pipeline of robotic harvesting system development—from deep learning-based fruit detection and 3D localization to mechanical end-effector design and complete field-deployable systems. Chu's most influential contributions include pioneering apple detection frameworks such as his Suppression Mask R-CNN approach (154 citations) and the occluder-occludee relational network O2RNet, designed to handle the visual complexity of clustered orchard environments. His systems-level research, including single- and dual-arm robotic apple harvesters evaluated under real field conditions, has garnered over 200 combined citations, demonstrating broad impact across both computer science and agricultural engineering communities. His innovative active laser-camera scanning methods push the boundaries of precise fruit localization beyond what standard depth-sensing approaches offer. Collectively accumulating nearly 500 citations across a focused and rapidly expanding body of work, Chu has established himself as a pivotal figure in harvest robotics, providing both algorithmic foundations and practical engineering solutions that directly advance the sustainability of modern agriculture.
Research Focus
Key Achievements
Top Papers
- 1Deep learning-based apple detection using a suppression mask R-CNN154 citations · 2021
- 2System design and control of an apple harvesting robot131 citations · 2021
- 3An automated apple harvesting robot—From system design to field evaluation81 citations · 2023
- 4Development and evaluation of a dual-arm robotic apple harvesting system27 citations · 2024
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- 6Algorithm Design and Integration for a Robotic Apple Harvesting System26 citations · 2022
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