Papers
1
Total Citations
99
H-Index
1
About
Li Qiao is a leading researcher in computer vision and agricultural automation, with a focus on deep learning-based object detection for precision farming. Her most-cited work, "Fast and accurate green pepper detection in complex backgrounds via an improved Yolov4-tiny model" (2021), has garnered 99 citations, reflecting its significant impact on the field. In this study, Qiao developed a lightweight yet highly accurate detection system that addresses the challenge of identifying green peppers in cluttered, natural environments—a task critical for automated harvesting and yield estimation. By enhancing the Yolov4-tiny architecture with attention mechanisms and feature fusion, she achieved a balance between speed and precision, enabling real-time performance on resource-constrained devices. This contribution not only advances agricultural robotics but also provides a scalable framework for detecting other crops in complex settings. Qiao’s work is notable for its practical applicability, bridging the gap between cutting-edge AI and real-world farming needs. Her research continues to inspire innovations in smart agriculture, making her a key figure in the intersection of computer vision and sustainable food production.
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Top Papers
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