Papers
15
Total Citations
380
H-Index
10
About
Yongliang Qiao is a prominent researcher at the intersection of artificial intelligence, robotics, and precision agriculture, whose work is reshaping how autonomous systems interact with farming environments. His research focuses on deep learning-based computer vision, agricultural robotics, and plant phenotyping, with particular emphasis on enabling robots to perceive, navigate, and act intelligently in complex crop environments. Qiao's most influential contribution — a 2021 paper on data augmentation for semantic segmentation and crop-weed classification (129 citations) — established foundational techniques for training robust agricultural AI models with limited labeled data. His subsequent work on ryegrass detection in wheat farms (58 citations) demonstrated real-time deep learning applicability in challenging field conditions. Through innovative projects like LettuceTrack and LettuceMOT, he has advanced precision spray robotics, enabling targeted chemical application that reduces environmental impact and operational costs. His research on spectral recovery from RGB images for maize disease detection further highlights his creativity in bridging affordable sensing with high-accuracy diagnostics. As an editorial leader in *Frontiers in Plant Science*, Qiao has helped shape the scientific conversation around AI-driven agriculture. With over 360 cumulative citations, his work continues to influence both academic research and the practical development of next-generation agricultural robotics systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Real time detection of inter-row ryegrass in wheat farms using deep learning58 citations · 2021
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- 5Maize disease detection based on spectral recovery from RGB images29 citations · 2022
- 6ConvNet and LSH-Based Visual Localization Using Localized Sequence Matching19 citations · 2019
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- 9AI, Sensors, and Robotics for Smart Agriculture12 citations · 2024
- 10