Yongting Tao

About

Yongting Tao’s research lies at the intersection of agricultural robotics, computer vision, and intelligent automation, with a primary focus on enabling robots to perceive, recognize, and interact with soft-bodied fruits and vegetables in unstructured environments. Her most influential work, “Automatic apple recognition based on the fusion of color and 3D feature for robotic fruit picking” (2017), has garnered 150 citations, establishing a foundational method for combining color and spatial data to improve fruit detection accuracy in real orchard settings. Tao further advanced the field by addressing the mechanical challenge of damage-free harvesting. In her study on robotic tomato grasping, she modeled the viscoelastic behavior of tomatoes using Burgers’ theory, proposing a real-time strategy to estimate material parameters for optimal, non-destructive gripping. Additionally, her work on segmenting apple tree point clouds from real scenes—fusing color and 3D features—demonstrated a robust pipeline for autonomous fruit recognition in complex, natural backgrounds. Through these contributions, Tao has significantly advanced the practical deployment of agricultural robots, bridging the gap between sensory perception and delicate manipulation, and her research continues to inform the design of smarter, safer harvesting systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Automatic apple recognition based on the fusion of color and 3D feature for robotic fruit picking

   150 citations · 2017
2. 2
   An optimum strategy for robotic tomato grasping based on real-time viscoelastic parameters estimation

   8 citations · 2017
3. 3
   An Automatic Segmentation and Recognition Method of Apple Tree Point clouds in the Real Scene Based on the Fusion of Color and 3D Feature

   4 citations · 2017

Key Collaborators

Contact & Links