Xinyuan Tian
Papers
2
Total Citations
25
H-Index
2
About
Xinyuan Tian is a leading researcher at the intersection of digital agriculture, robotics, and computer vision. Their work centers on developing intelligent, automated solutions for horticultural production, with a particular focus on reinforcement learning and deep learning for robotic manipulation and environmental monitoring. Tian’s most influential contribution is a pioneering framework for training fruit-picking robot arms using reinforcement learning within a digital twin environment—a paper that has garnered 20 citations since 2023 and is foundational to the Industry 4.0 transformation of agriculture. More recently, Tian has advanced the field of agricultural waste management by introducing a lightweight YOLOv7-based model for detecting and classifying orchard garbage, including pruning debris and non-biodegradable materials like pesticide containers. This work, published in 2025, addresses a critical gap in soil sustainability and horticultural waste disposal. By combining digital twin simulation with efficient deep learning architectures, Tian’s research offers scalable, real-world solutions for autonomous farming, making significant strides toward reducing labor costs and environmental impact in modern agriculture.
Research Focus
Key Achievements
Top Papers
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- 2