Jing Pang

About

Jing Pang is a leading researcher in agricultural robotics and computer vision, specializing in automated fruit detection and harvesting systems for complex orchard environments. Her work integrates deep learning with RGB and RGB-D imaging to solve critical challenges in precision agriculture. Pang’s most influential contribution is the development of a multiple-scale Faster R-CNN architecture for passion fruit detection and counting, which achieved 154 citations by pioneering the use of depth information to improve accuracy in cluttered canopies. She further advanced the field with a mango picking vision algorithm that combines instance segmentation and key point detection from RGB images alone, cited 88 times, enabling robots to identify both fruit location and optimal grasping points in open orchards. Her research addresses the practical hurdles of variable lighting, occlusion, and fruit maturity, directly impacting automated harvesting efficiency. Pang’s work is notable for bridging the gap between laboratory computer vision models and real-world agricultural deployment, making her a key figure in the push toward fully autonomous fruit picking systems. Her algorithms continue to influence subsequent studies in agricultural robotics and smart 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
   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