Papers
1
Total Citations
95
H-Index
1
About
Xingshi Xu is a leading researcher in agricultural artificial intelligence, specializing in deep learning applications for precision horticulture. His work centers on developing lightweight, real-time computer vision algorithms that enable automated detection and monitoring of fruit crops in complex natural environments. Xu’s most-cited paper, "Using lightweight deep learning algorithm for real-time detection of apple flowers in natural environments" (2023), has garnered 95 citations, demonstrating its significant impact on the field. This contribution addresses a critical bottleneck in smart agriculture: achieving high-accuracy detection with minimal computational resources, making it feasible for deployment on edge devices in orchards. By optimizing neural network architectures for speed and efficiency, Xu’s research directly supports yield estimation, pollination monitoring, and robotic harvesting systems. His work bridges the gap between state-of-the-art AI and practical, scalable agricultural solutions, earning recognition from both computer science and agronomy communities. Xu’s innovations are paving the way for more sustainable, data-driven farming practices, and his algorithms are increasingly adopted in precision agriculture research worldwide.
Research Focus
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Top Papers
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