Tianqi Yao is a Ph.D. student in the Precision and Automated Agriculture Lab (PAAL) under the supervision of Dr. Jianfeng Zhou. His research integrates sensor technologies, artificial intelligence (AI), edge computing, and remote data collection to create automated solutions for agricultural pest monitoring and management. His current project focuses on developing an intelligent system to monitor Spotted Wing Drosophila (SWD), a major agricultural pest, using high-resolution cameras, AI-powered computer vision algorithms, and environmental sensors to detect and track insect populations in real time.
The system transmits data from field traps over LoRa and cellular networks, processes it at the edge, and shares it via the cloud, providing farmers with real-time insights through a web platform. By integrating Growing Degree Day (GDD) models, the system predicts pest population trends and enhances Integrated Pest Management (IPM) with timely, data-driven recommendations. Leveraging edge computing and IoT-enabled devices, Tianqi’s work aims to establish a scalable and efficient pest monitoring network.
Research Interests
- Sensing technologies and AI in agriculture
- Computer vision for pest detection
- IoT, LoRa, and cellular networks in precision agriculture
- Integrated Pest Management (IPM)
- Remote sensing and wireless sensor networks
- Edge computing in agricultural systems
Keywords：
Artificial Intelligence (AI), Computer Vision, Edge Computing, Internet of Things (IoT), Sensor Technologies, LoRa, Cellular Networks, Precision Agriculture, Integrated Pest Management (IPM), Pest Monitoring, Spotted Wing Drosophila (SWD)
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