Yingqi Cai
Papers
1
Total Citations
14
H-Index
1
About
Yingqi Cai is a researcher at the forefront of agricultural robotics and intelligent perception, with a primary focus on developing advanced computer vision systems for precision harvesting. Their most notable contribution is the development of an improved YOLOv7-Tiny neural network for cherry tomato detection, which integrates multimodal perception to significantly enhance both accuracy and efficiency in real-world farming environments. This work, published in 2024 and already garnering 14 citations, directly addresses critical challenges in robotic fruit harvesting—such as occlusions, variable lighting, and the need for real-time processing—by optimizing lightweight deep learning architectures for embedded agricultural robots. Cai’s research bridges the gap between state-of-the-art object detection algorithms and practical deployment in unstructured agricultural settings, demonstrating how tailored neural networks can enable reliable, high-speed fruit identification essential for autonomous harvesting systems. By tackling the specific difficulties of detecting small, clustered cherry tomatoes, Cai’s innovations contribute to the broader goal of revolutionizing agriculture through robotics, making automated harvesting more viable and efficient for the farming industry.
Research Focus
Key Achievements
Top Papers
- 1