Xiaodan Peng
Papers
2
Total Citations
89
H-Index
2
About
Xiaodan Peng is a leading researcher in agricultural artificial intelligence and precision horticulture, with a focus on computer vision systems for fruit maturity detection. Her work addresses the critical challenge of automating harvest timing in orchard environments, particularly for high-value oil crops. Peng’s major contributions include developing modified lightweight YOLO architectures for real-time Camellia oleifera fruit maturity assessment, achieving 55 citations for her 2024 study, and creating the Olive-EfficientDet model for multi-cultivar olive fruit detection, cited 34 times since 2023. These innovations enable accurate, non-destructive maturity classification under complex orchard conditions, significantly reducing labor costs and post-harvest losses. Her research integrates deep learning with agricultural engineering, producing models that balance detection accuracy with computational efficiency for deployment on edge devices. Peng’s work has been recognized for its practical impact on smart agriculture, providing scalable solutions for fruit quality assessment and harvest scheduling. Her ongoing research continues to advance the frontiers of precision agriculture, making her a notable figure in the intersection of AI and sustainable crop management.
Research Focus
Key Achievements
Top Papers
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