PhD Student · Doctoral Researcher
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University — and the National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology (NPAAC), Guangzhou, China.
About
I am a PhD student at the College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, and a doctoral researcher at the National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology (NPAAC).
My research sits at the intersection of precision agricultural aviation, electronic nose (E-nose) sensing, and machine learning. I work on unmanned aerial spraying systems (UASS) and intelligent sensing technologies for smart agriculture and Agri-IoT — from optimizing droplet deposition and drift control to building data-driven detection models for food quality and crop protection.
Beyond research, I am an active student leader: I serve as Co-Chair of the Guangzhou Students' Federation and was formerly Co-Chair (Research Affairs) of the SCAU Graduate Student Union, where I founded and presided over the university's 1st Graduate Academic Conference, which drew over 1,000 participants and more than 800 conference submissions. I am also one of the Technological Innovation Spokespersons of Guangzhou TV Station.
Recognized as a Beijing Outstanding Graduate and a recipient of the First-Class Doctoral Scholarship, I have earned nearly 20 honors and hold memberships in the China Association of Agricultural Science Societies (Smart Agriculture) and IEEE Agricultural Robotics and Automation.
Electronic Nose
UAV / UASS
Machine Learning
Agri-IoT
Research Interests
Applying sensing, robotics, and learning to make agriculture more precise and sustainable.
Gas-sensor arrays and pattern recognition for rapid, non-destructive detection of food freshness and quality.
Unmanned aerial spraying systems (UASS): droplet-size control, deposition quality, and drift-risk optimization.
Data-fusion models, time-series prediction, and zero-shot large models for detection and quantification.
Smart monitoring systems (e.g., NB-IoT) and portable spectroscopy for connected, data-driven agriculture.
Education
Experience
Application of Zero-Shot Large Models for Fruit Object Detection in Smart Agriculture
Optimization of spraying quality and drift risk in unmanned aerial spraying systems (UASS) based on multi-gradient droplet size control
Field Evaluation of Different Unmanned Aerial Spraying Systems Applied to Control Panonychus citri in Mountainous Citrus Orchards
Time series model for predicting the disturbance of lychee canopy by wind field in unmanned aerial spraying system
Automated detection of stale beef from electronic nose data
Rapid detection of adulterated lamb meat using near infrared and electronic nose: A F1-score-MRE data fusion approach
Using a Portable Visible-Near Infrared Spectrometer and Machine Learning to Distinguish and Quantify Mold Contamination in Wheat
Development of electronic nose for detection of micro-mechanical damages in strawberries
Real-Time Monitoring System for Rabbit House Environment Based on NB-IoT Network
A method and device for detecting the occurrence time of mechanical damage in strawberries
Peer Review
I serve as a peer reviewer for the following journals.
Contact
Find me and my research through these channels.