Calvin Hung

The University of Sydney

Papers

5

Total Citations

294

H-Index

5

About

Calvin Hung is a leading researcher in agricultural robotics and precision horticulture, with a focus on developing autonomous systems for crop monitoring and management. His work integrates lidar, multi-spectral imaging, and machine learning to solve critical challenges in orchard and field agriculture. Hung’s most cited paper, “Mapping almond orchard canopy volume, flowers, fruit and yield using lidar and vision sensors” (179 citations), pioneered the use of sensor fusion for non-destructive yield estimation, enabling growers to optimize harvest planning. He also advanced fruit segmentation with a multi-spectral feature learning approach (97 citations), which automated the detection of fruit in complex orchard environments, outperforming traditional hand-crafted methods. Beyond orchards, Hung applied robotic aircraft and intelligent surveillance systems for weed detection, targeting invasive species like orange hawkweed and prickly acacia. His work on autonomous fruit yield estimation systems, presented at the 2016 International Horticultural Congress, further demonstrates his impact on precision agriculture. With over 290 total citations, Hung’s contributions are instrumental in bridging robotics, computer vision, and sustainable farming, offering scalable solutions for food production and environmental management.

Research Focus

Key Achievements

5
H-Index
5
Papers
294
Total Citations
59
Avg Citations/Paper
🏆 Most Cited Paper
Mapping almond orchard canopy volume, flowers, fruit and yield using lidar and vision sensors
179 citations · 2016
📈 Most Prolific Year: 2016 (2 Papers)
🤝 Key Collaborators: 5
🏛 Institutions: The University of Sydney

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago