Xinxin Zhu

Henan Institute of Science and Technology, Jiangsu University

Papers

Total Citations

H-Index

About

Xinxin Zhu is a leading researcher in agricultural robotics and computer vision, specializing in automated fruit detection and yield estimation for orchard environments. Her work addresses critical challenges in precision agriculture, particularly the limitations of convolutional neural networks for object detection in complex, real-world settings. Zhu’s most cited paper (2024, 30 citations) introduces an improved Faster-RCNN model for apple detection, overcoming inductive biases that hinder traditional deep learning approaches in cluttered orchard scenes. Her earlier research (2018, 26 citations) pioneered a close-shot identification method for on-branch citrus fruit using Intel RealSense depth cameras, enabling rapid and reliable recognition for robotic harvesting—a breakthrough that directly addressed the shortcomings of existing depth-based fruit recognition systems. With a cumulative impact of over 56 citations across her top works, Zhu’s contributions are foundational to the development of autonomous agricultural systems. Her innovative fusion of depth sensing and deep learning has set new benchmarks for fruit detection accuracy and speed, making her work essential reading for researchers in agricultural AI, field robotics, and computer vision applications in unstructured environments.