Shuqin Tu

About

Dr. Shuqin Tu is a leading researcher in agricultural computer vision and precision horticulture, whose work focuses on automating fruit detection, maturity classification, and robotic harvesting. Her major contributions lie in developing deep learning-based vision systems that integrate RGB-D (Red-Green-Blue Depth) imaging with advanced architectures like Faster R-CNN and instance segmentation. Her most influential paper, "Passion fruit detection and counting based on multiple scale faster R-CNN using RGB-D images" (2020, 154 citations), introduced a multi-scale detection framework that significantly improved accuracy in cluttered orchard environments. This built on her earlier foundational study, "Detection of passion fruits and maturity classification using Red-Green-Blue Depth images" (2018, 141 citations), which pioneered the use of depth data for both detection and ripeness assessment. Her 2021 work on mango picking vision, employing key point detection from RGB images in open orchards (88 citations), further demonstrates her versatility in tackling real-world agricultural challenges. Collectively, her research has garnered over 380 citations, establishing her as a key figure in bridging computer vision and smart agriculture, with direct implications for reducing labor costs and improving yield estimation in fruit farming.

Research Focus

Key Achievements

Top Papers

1. 1
   Passion fruit detection and counting based on multiple scale faster R-CNN using RGB-D images

   154 citations · 2020
2. 2
   Detection of passion fruits and maturity classification using Red-Green-Blue Depth images

   141 citations · 2018
3. 3
   A mango picking vision algorithm on instance segmentation and key point detection from RGB images in an open orchard

   88 citations · 2021

Key Collaborators

Contact & Links