Jingyao Gai
Papers
23
Total Citations
630
H-Index
11
About
Jingyao Gai is a pioneering researcher at the intersection of agricultural robotics, computer vision, and precision agriculture. His work focuses on developing intelligent robotic systems for autonomous navigation, weed control, and plant phenotyping — critical challenges in modernizing crop production. Gai's most influential contributions center on enabling robots to perceive and navigate complex agricultural environments. His depth-camera-based crop row detection system (111 citations) and Double-DQN path smoothing method (107 citations) have advanced autonomous under-canopy navigation, while his color-depth image fusion approach for crop plant detection (110 citations) laid important groundwork for robotic weeding systems. His 4WD/4WS agricultural vehicle navigation research (65 citations) further demonstrates expertise in robust field robotics. Beyond navigation, Gai has expanded into automated plant phenotyping, developing systems for maize leaf angle characterization using stereo vision and deep learning, robotic drought-response assays, and growth-chamber phenotyping platforms — tools that accelerate understanding of genotype-environment interactions. His fruit-tree mapping system using multi-sensor SLAM reflects a broadening vision toward orchard automation. With hundreds of citations across interconnected domains, Gai's body of work represents a cohesive and impactful research program transforming how robots perceive, navigate, and interact with agricultural ecosystems.
Research Focus
Key Achievements
Top Papers
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- 4Robust navigation control of a 4WD/4WS agricultural robotic vehicle65 citations · 2019
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- 6The use of agricultural robots in weed management and control47 citations · 2019
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- 8Plant Recognition through the Fusion of 2D and 3D Images for Robotic Weeding17 citations · 2015
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