Chao Qi

Nanjing Agricultural University, Huazhong University of Science and Technology

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About

Chao Qi is pioneering the intersection of agricultural robotics and deep learning, with a primary focus on intelligent detection systems for specialty crops. His research centers on developing lightweight neural network architectures for real-time chrysanthemum detection in complex field environments—a critical capability for selective harvesting robots. Qi’s most impactful work, the TC-YOLO model for tea chrysanthemum detection under unstructured conditions, has garnered 60 citations and established a foundation for precision agriculture applications. He has further advanced the field with the MC-LCNN model for medicinal chrysanthemum detection, addressing the challenge of accurate, real-time identification in dense, variable field settings. Expanding beyond detection, Qi has explored vision transformer-based approaches for early tea chrysanthemum flower counting, overcoming the limitations of conventional CNNs in global feature extraction. His earlier work on HTN-based multi-robot path planning, with its conflict resolution and time constraint mechanisms, demonstrates versatility in addressing broader robotics challenges. Through these contributions, Qi is enabling the next generation of autonomous agricultural systems that can operate reliably in the unstructured, dynamic environments where traditional computer vision approaches fall short.