Papers
1
Total Citations
32
H-Index
1
About
Haiou Guan is a leading researcher in agricultural artificial intelligence and precision phenotyping, with a focus on deep learning-based object detection for crop monitoring. His most-cited work, "Tomato fruit detection and phenotype calculation method based on the improved RTDETR model" (2024), has already garnered 32 citations, reflecting its immediate impact on the field. Guan’s major contribution lies in advancing real-time, high-accuracy detection models for fruit identification and phenotypic trait extraction, addressing critical challenges in automated agriculture. By enhancing the RTDETR architecture, he has enabled more reliable yield estimation and growth analysis, directly supporting smart farming practices. His research bridges computer vision and plant science, offering scalable solutions for non-destructive crop assessment. Guan’s work is notable for its practical applicability, with potential to revolutionize greenhouse and field-based monitoring systems. Through his innovative approaches, he continues to shape the future of precision agriculture, making his research essential reading for students and scholars exploring AI-driven agricultural technologies.
Research Focus
Key Achievements
Top Papers
- 1