Yingjian Hou
Papers
1
Total Citations
22
H-Index
1
About
Yingjian Hou is a researcher at the forefront of intelligent agricultural technology, with a primary focus on computer vision and deep learning for precision fruit detection. Hou’s major contribution lies in advancing lightweight, high-accuracy object detection models tailored for complex orchard environments. In the highly cited work “Improved YOLOv7-Tiny Complex Environment Citrus Detection Based on Lightweighting” (2023, 22 citations), Hou addressed the critical challenge of detecting citrus fruits under real-world conditions—such as variable lighting, branch occlusion, and fruit overlap. By introducing the YOLO-DCA model, which replaces standard convolutions with depth-separable convolutions (DWConv), Hou achieved a significant reduction in model complexity without sacrificing detection performance. This innovation not only improves the feasibility of deploying detection systems on resource-constrained devices but also enhances the robustness of automated harvesting and yield estimation. Hou’s work has garnered attention for its practical impact on smart agriculture, bridging the gap between state-of-the-art deep learning and real-world farming needs. With a growing citation record, Yingjian Hou is establishing a reputation for developing efficient, deployable solutions that push the boundaries of agricultural automation.
Research Focus
Key Achievements
Top Papers
- 1