Ruijie Fu

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About

Ruijie Fu is a researcher at the forefront of agricultural robotics and computer vision, whose work addresses the critical challenge of precision detection in complex natural environments. Fu’s primary research focuses on developing advanced deep learning algorithms for small object detection, with a particular emphasis on automating tea harvesting—a task that demands exceptional accuracy to preserve crop quality. In their most-cited work, "Small object detection algorithm incorporating swin transformer for tea buds" (2024, 16 citations), Fu introduced a novel hybrid architecture that integrates Swin Transformer’s powerful attention mechanisms with convolutional neural networks to overcome the difficulties of identifying tiny, occluded tea buds against cluttered, variable backgrounds. This contribution directly enables more reliable perception for harvesting robots, offering a tangible solution to a longstanding bottleneck in agricultural automation. By bridging state-of-the-art transformer models with real-world agrotech needs, Fu’s research not only advances the field of object detection but also demonstrates a clear pathway from algorithmic innovation to practical, yield-enhancing technology. Their work stands as a valuable reference for researchers exploring vision-based automation in unstructured outdoor settings.