Renfu Lu

About

Renfu Lu is a leading researcher in agricultural robotics and precision automation, with a particular focus on solving one of the most pressing challenges facing modern horticulture: the automation of fruit harvesting. Working primarily within the context of the apple industry, Lu has made pioneering contributions to the full pipeline of robotic harvesting systems—from intelligent perception to mechanical end-effector design and integrated field deployment. Lu's most influential work centers on deep learning-based fruit detection, including the development of a suppression Mask R-CNN for apple detection (154 citations) and the occluder-occludee relational network O2RNet, designed to handle the visual complexity of clustered orchard environments. Complementing these perception advances, his team engineered complete robotic harvesting systems—including a dual-arm harvester and vacuum-based end effectors—bridging the gap between laboratory algorithms and real-world agricultural deployment (131 citations). A distinctive thread in Lu's research is high-precision fruit localization using active laser-camera scanning, addressing a critical yet underexplored bottleneck in harvest robotics. His more recent work on agricultural foundation models signals an ambitious expansion toward generalizable AI across multiple fruit crops. Collectively accumulating over 500 citations, Lu's body of work represents some of the most comprehensive and field-validated research in agricultural automation today.

Research Focus

Key Achievements

Top Papers

1. 1
   Deep learning-based apple detection using a suppression mask R-CNN

   154 citations · 2021
2. 2
   System design and control of an apple harvesting robot

   131 citations · 2021
3. 3
   An automated apple harvesting robot—From system design to field evaluation

   81 citations · 2023
4. 4
   Development and evaluation of a dual-arm robotic apple harvesting system

   27 citations · 2024
5. 5
   O2RNet: Occluder-occludee relational network for robust apple detection in clustered orchard environments

   27 citations · 2023
6. 6
   Algorithm Design and Integration for a Robotic Apple Harvesting System

   26 citations · 2022
7. 7
   Design and Evaluation of End Effectors for a Vacuum-Based Robotic Apple Harvester

   22 citations · 2022
8. 8
   Active Laser-Camera Scanning for High-Precision Fruit Localization in Robotic Harvesting: System Design and Calibration

   15 citations · 2023
9. 9
   High-precision fruit localization using active laser-camera scanning: Robust laser line extraction for 2D-3D transformation

   12 citations · 2024
10. 10
    MetaFruit meets foundation models: Leveraging a comprehensive multi-fruit dataset for advancing agricultural foundation models

    10 citations · 2025

Key Collaborators

Contact & Links