Digital Agriculture & Robotics Lab.
Our lab focuses on sensor development and deployment, mechanical and digital control systems, and data engineering, as well as autonomous robotic systems which employ machine vision, artificial intelligence, and other emerging technologies for phenotyping (plant and animal), food quality assessment, and other relevant applications in agriculture.
Location: Room 307, Building No. 1, College of Agriculture and Life Sciences
Indoor Farming and Greenhouse Automation Systems
- Automatic operations based on motion planning, mission control, and obstacle localization
- Intelligent manipulator and end effector for harvesting, spraying, and monitoring
- Integrated vision systems to detect fruits and assess ripeness and quality with machine learning
Automated and Connected Agricultural Robotic Systems
- Locomotion in dynamic and semi-structured environments
- Localization and mapping based on a combination of GPS, INS, LiDAR, and computer vision
- Machine learning-based computer vision for adaptation to seasonal changes, new emerging diseases, and pests
- Coordination of fleets of field robots based on peer-to-peer communication method
Unmanned Aerial Vehicle-based Remote Sensing for Field Phenotyping
- Spatial variability of crop yield, terrain features, and moisture level for farm management
- Crop or vegetation maps for biomass estimation, yield prediction, and crop infestation monitoring
- Synchronized motion control algorithms for a pair of UAVs real-time collision avoidance
- Optimal adaptive mission planning for collaborative operations of multiple heterogeneous vehicles
The Internet of Things (IoT) in Agriculture
- Wireless sensor-based network that connects in-field or greenhouse sensors and weather stations
- Cloud-based big data management systems for storage, visualization, and analyses
- Cloud-based machine learning modules for indoor or outdoor farm decision-making