Papers
4
Total Citations
151
H-Index
4
About
Pingzeng Liu is a leading researcher at the intersection of agricultural robotics and deep learning, with a primary focus on intelligent harvesting and post-harvest processing. His work addresses critical challenges in precision agriculture, particularly the automated detection and maturity classification of fruit in complex, natural greenhouse environments. Liu’s most impactful contributions involve adapting state-of-the-art computer vision algorithms for agricultural tasks. His seminal 2021 paper on using Mask R-CNN for detecting and segmenting mature green tomatoes—a notoriously difficult task due to their color similarity to foliage—has garnered 69 citations. He further advanced the field with a 2022 study on a lightweight SE-YOLOv3-MobileNetV1 network for tomato maturity classification, which has received 62 citations. Beyond vision systems, Liu has developed multi-sensor fusion architectures for agricultural robot obstacle avoidance and, most recently, a specialized YOLOv5-based robot for precise mushroom grading and metering. His cumulative work demonstrates a sustained commitment to deploying robust, real-time AI solutions that bridge the gap between laboratory algorithms and practical, in-field agricultural automation.
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