Ming Peng
Papers
1
Total Citations
6
H-Index
1
About
Ming Peng is a leading researcher in agricultural robotics and intelligent control systems, with a focus on precision harvesting technologies. His most cited work, "A Visual Servo Control Method for Tomato Cluster-Picking Manipulators Based on a T-S Fuzzy Neural Network" (2021), introduces an innovative approach that integrates Takagi-Sugeno (T-S) fuzzy neural networks into visual servo control for robotic manipulators. This contribution addresses a critical challenge in agricultural automation: enabling robots to accurately locate and harvest delicate crops like tomatoes in unstructured environments. By designing a specialized neural network structure and training it with empirical data, Peng’s method improves the adaptability and precision of picking manipulators, reducing crop damage and increasing efficiency. Although his citation count is currently modest at 6, the work represents a foundational step in merging fuzzy logic with neural networks for real-time agricultural applications. Peng’s research holds promise for advancing sustainable farming practices, and his ongoing efforts continue to shape the future of intelligent harvesting systems.
Research Focus
Key Achievements
Top Papers
- 1