Yikun Liu
Papers
1
Total Citations
18
H-Index
1
About
Yikun Liu is a researcher at the forefront of agricultural robotics and computer vision, with a primary focus on developing intelligent systems for precision farming. His most-cited work, “A Transformer-based mask R-CNN for tomato detection and segmentation” (2023, 18 citations), addresses a critical bottleneck in automated harvesting: the reliable detection of fruit under challenging field conditions. By integrating Transformer architectures with mask region-based convolutional neural networks, Liu’s model significantly improves segmentation accuracy in the presence of illumination variation and occlusion—two pervasive obstacles in unstructured agricultural environments. This contribution not only advances the state of the art in deep learning for agri-robotics but also provides a practical foundation for real-time, vision-guided harvesting platforms. His research bridges the gap between high-performance AI models and real-world deployment, demonstrating how attention mechanisms can enhance feature extraction for small, deformable objects like tomatoes. Liu’s work is increasingly cited by teams developing autonomous picking systems, underscoring its impact on the intersection of machine learning and sustainable agriculture.
Research Focus
Key Achievements
Top Papers
- 1A transformer-based mask R-CNN for tomato detection and segmentation18 citations · 2023