Yang Caijuan
Papers
1
Total Citations
17
H-Index
1
About
Yang Caijuan is a researcher at the forefront of agricultural artificial intelligence, specializing in deep learning and computer vision for precision agriculture. Her primary research focuses on developing lightweight, efficient detection models for tea bud identification, a critical task for automated tea harvesting. In her most-cited work, "Tea bud DG: A lightweight tea bud detection model based on dynamic detection head and adaptive loss function" (2024), she introduced a novel architecture that balances accuracy and computational efficiency, addressing the challenges of real-time field detection. This model, which has garnered 17 citations in a short time, employs a dynamic detection head to adapt to varying bud sizes and an adaptive loss function to improve localization precision. Yang’s contributions are significant for advancing smart agriculture, enabling cost-effective robotic harvesting systems that reduce labor dependency. Her work demonstrates a clear impact on the intersection of AI and agronomy, with potential to transform tea cultivation practices. As an emerging voice in this niche, Yang Caijuan is paving the way for more accessible, high-performance agricultural technologies.
Research Focus
Key Achievements
Top Papers
- 1