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About

Zhenyu Zhong is a researcher at the forefront of agricultural robotics, computer vision, and deep learning safety. His work primarily focuses on bridging the gap between artificial intelligence and real-world, unstructured environments—from litchi orchards to adversarial attack scenarios. Zhong’s most impactful contribution is his pioneering approach to automatic fruit harvesting, as detailed in his highly cited 2019 paper on computer vision-based localisation of picking points for litchi harvesting (69 citations). This work directly addresses the challenge of deploying AI in natural, dynamic agricultural settings, offering a practical pathway toward automated crop collection. Beyond agriculture, Zhong has made notable strides in AI security, co-authoring a study on robust physical adversarial attacks against YOLO object detectors (16 citations), exploring how to fool deep learning models in the physical world—a critical step toward building safer autonomous systems. His research also encompasses improvements to lidar-based simultaneous localization and mapping (SLAM) and efficient object detection model pruning for applications like municipal waste classification. Through these diverse contributions, Zhong demonstrates a commitment to making AI both more capable and more resilient in complex, real-world applications.