Sihui Dai
Papers
4
Total Citations
68
H-Index
4
About
Sihui Dai is a leading researcher in agricultural robotics and computer vision, specializing in deep learning-based fruit detection and localization for automated harvesting systems. Her work addresses critical labor shortages in agriculture by developing robust, real-time recognition algorithms for picking robots. Dai’s most impactful contribution is a coupled YOLO/Mask R-CNN framework for strawberry detection from 3D binocular cameras, achieving 31 citations by enabling accurate localization of mature, occluded, and clustered fruits. She further advanced lightweight detection with an improved YOLOv4-GhostNet for kiwifruit in orchards (17 citations) and a modified YOLOv3 for citrus under complex field conditions (15 citations), demonstrating her ability to balance speed and accuracy for edge deployment. Beyond fruit detection, Dai innovated in quality assessment by automating Brix measurement for watermelon breeding, reducing labor-intensive sampling. Her work consistently pushes the boundaries of precision agriculture, integrating state-of-the-art neural architectures with practical robotic applications. With over 68 cumulative citations, Dai’s research is foundational for next-generation autonomous farming, offering scalable solutions that enhance both harvesting efficiency and crop quality evaluation.
Research Focus
Key Achievements
Top Papers
- 1
- 2Kiwifruit Detection Method in Orchard via an Improved Light-Weight YOLOv417 citations · 2022
- 3
- 4Automatic Brix Measurement for Watermelon Breeding5 citations · 2022