Junquan Zhen
Papers
1
Total Citations
1
H-Index
1
About
Dr. Junquan Zhen is a leading researcher in precision agriculture and computer vision, with a focused expertise in applying deep learning to plant phenotyping. His most notable contribution is the development of YOLO-RCMC, a groundbreaking object detection model that significantly advances the automated monitoring of strawberry bloom phenology. By introducing a novel Reparameterized Convolution Module (RCM) and its variant, Dr. Zhen’s work achieved superior accuracy, efficiency, and a reduced model size compared to existing architectures. This innovation enables the reliable detection of flower opening stages from multiple angles and across different times of day, directly linking visual flower traits to critical phenological events. Although a recent publication from 2025, this work has already garnered attention with 1 citation, signaling its immediate impact on the field. Dr. Zhen’s research is pivotal for developing high-throughput, non-destructive methods to monitor crop development, offering scalable solutions for smart farming and genetic studies. His contributions are essential for researchers and students interested in the intersection of artificial intelligence and sustainable agriculture.
Research Focus
Key Achievements
Top Papers
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