Qinghua Lu

About

Qinghua Lu is a leading researcher in agricultural robotics, specializing in computer vision, deep learning, and autonomous manipulation for precision harvesting. Her work focuses on enabling robots to perceive and interact with complex, unstructured agricultural environments, particularly vineyards and orchards. Lu’s major contributions include developing a vision methodology for detecting cutting points on grape peduncles (133 citations), a collision-free path-planning algorithm for six-DOF harvesting robots using energy optimization and artificial potential fields (46 citations), and a deep learning-based lightweight network for grape recognition in occluded settings (22 citations). She has also advanced 3D detection of occluded stems for robotic harvesting (9 citations) and efficient semantic segmentation via neural architecture search (19 citations). With over 400 total citations across her top papers, Lu’s work directly addresses key challenges in agricultural automation—occlusion, dynamic environments, and real-time performance—making her a pivotal figure in the field. Her innovative approaches, such as combining point cloud segmentation with geometric analysis for grape cluster pose estimation (59 citations), have significantly improved the intelligence and reliability of harvesting robots, paving the way for more efficient and sustainable farming practices.

Research Focus

Key Achievements

Top Papers

1. 1
   A vision methodology for harvesting robot to detect cutting points on peduncles of double overlapping grape clusters in a vineyard

   133 citations · 2018
2. 2
   In-field pose estimation of grape clusters with combined point cloud segmentation and geometric analysis

   59 citations · 2022
3. 3
   Recognition of sweet peppers and planning the robotic picking sequence in high-density orchards

   53 citations · 2022
4. 4
   Collision‐Free Path‐Planning for Six‐DOF Serial Harvesting Robot Based on Energy Optimal and Artificial Potential Field

   46 citations · 2018
5. 5
   Grape Berry Detection and Size Measurement Based on Edge Image Processing and Geometric Morphology

   45 citations · 2021
6. 6
   An improved lightweight network based on deep learning for grape recognition in unstructured environments

   22 citations · 2023
7. 7
   M-FasterSeg: An efficient semantic segmentation network based on neural architecture search

   19 citations · 2022
8. 8
   Learning invariant semantic representation for long-term robust visual localization

   11 citations · 2022
9. 9
   DRL-enhanced 3D detection of occluded stems for robotic grape harvesting

   9 citations · 2024
10. 10
    A Novel Efficient Convolutional Neural Algorithm for Multi-Category Aliasing Hardware Recognition

    6 citations · 2022

Key Collaborators

Contact & Links