Huang Jiewei
Papers
1
Total Citations
17
H-Index
1
About
Huang Jiewei is a researcher whose work lies at the intersection of computer vision and agricultural automation, with a particular focus on deep learning for precision farming. His most-cited contribution, "Tea bud DG: A lightweight tea bud detection model based on dynamic detection head and adaptive loss function" (2024, 17 citations), introduces a novel, efficient approach to identifying tea buds in complex field environments. This model addresses a critical bottleneck in smart agriculture by balancing detection accuracy with computational efficiency, making it suitable for deployment on resource-constrained devices. Huang’s key innovation lies in the integration of a dynamic detection head that adapts to varying bud sizes and an adaptive loss function that improves localization precision. By tackling the challenges of occlusion, scale variation, and real-time processing in tea plantations, his work has significant implications for automated harvesting and yield estimation. Though early in his career, Huang’s targeted contributions to lightweight, high-performance detection systems position him as an emerging voice in agricultural AI, bridging the gap between cutting-edge deep learning and practical, field-ready solutions.
Research Focus
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Top Papers
- 1