Agricultural Robotics & Computer Vision
3D plant reconstruction, point-cloud structural analysis, robotic sensing, and autonomous data collection for high-throughput phenotyping.
Curriculum vitae · Updated July 2026
Liangchao Deng邓良超
Postdoctoral Researcher
Shenzhen Institute of China Agricultural University · Shenzhen, China
Research at the intersection of AI-driven plant phenotyping, 3D computer vision, remote sensing, and process-based crop simulation.
Ph.D. in Crop Science · Shihezi University · 2026Appointment · 1 Aug 2026 – 31 Jul 2029
- research themes
- 5
- Ph.D. awarded
- 2026
- postdoctoral term
- 2026–29
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Research profile
Five connected research themes
A research program connecting field sensing, scientific computing, and deployable agricultural systems.
AI-driven Plant Phenotyping
Multimodal AI for image segmentation, object detection, phenotypic analysis, and structure–function modeling.
Canopy Light & Photosynthesis
Physics-based canopy light interception and photosynthesis modeling for efficient crop research.
Remote Sensing & Sensor Fusion
RGB, multispectral, hyperspectral, and LiDAR data fusion for smart agriculture.
Digital Twins & Process Models
Coupling sensing data with crop growth models to build agricultural automation and simulation systems.
Current appointment
Professional appointment
1 August 2026 – 31 July 2029
Postdoctoral Researcher
Shenzhen Institute of China Agricultural University, Shenzhen, China
Postdoctoral research at the Shenzhen Institute of China Agricultural University.
Academic training
Education
2021 – 2026
Ph.D. in Crop Science (Integrated Master–Ph.D.)
Shihezi University, China
- Supervisors
- Prof. Yali Zhang; Dr. Qingfeng Song; Prof. Xin-Guang Zhu
- Research focus
- Crop phenomics, UAV remote sensing, canopy photosynthesis modeling, and AI-assisted phenotyping
- Joint training
- CAS Center for Excellence in Molecular Plant Sciences (CEMPS)
- 3D crop canopy reconstruction and light-distribution simulation using SfM and 3D Gaussian Splatting.
- Fusion of RGB, multispectral, hyperspectral, and LiDAR observations.
- Deep learning for segmentation, detection, and phenotypic-parameter prediction.
2016 – 2021
B.Sc. in Information and Computational Science
Shihezi University, China
- Foundation
- Numerical analysis, computational modeling, programming, and algorithm design
- Core courses
- Computer vision, machine learning, linear algebra, optimization, graph theory, and data structures
- Graduation project
- Computational-fluid-dynamics simulation (Excellent Graduation Project)
Selected work
Research experience
Selected programs showing the progression from sensing and reconstruction to simulation and applied phenotyping.
AI-assisted 3D Crop Canopy Modeling
3D reconstruction, light distribution & photosynthesis
- 3D canopy reconstruction
- High-throughput farmland reconstruction using SfM, 3D Gaussian Splatting, and UAV cross-circular acquisition, reaching centimeter-level accuracy.
- Light & photosynthesis
- Canopy-scale simulation using ray tracing and BRDF-based leaf optics within a crop digital-twin framework.
- Multimodal AI
- RGB, multispectral, and LiDAR workflows for complex-scene, zero-shot plant segmentation.
- Modular research agent
- Integrated reconstruction, meshing, canopy generation, light simulation, and photosynthesis calculation into reusable modules.
High-throughput 3D & Spectral Phenotyping
Optical inversion, crop design & computer vision
- Leaf optical inversion
- Developed a BRDF-based inversion framework and optimized measurement schemes for indirect leaf-optics estimation.
- Wheat design research
- Modeled plant architecture and cultivation configurations for higher canopy efficiency, including industry-oriented collaboration.
- Phenotyping algorithms
- Built computer-vision methods for automated extraction and analysis of plant phenotypic traits.
Research outputs
Publications & software
Peer-reviewed research and reusable software supporting plant phenotyping workflows.
Peer-reviewed article2025
Leaf Bidirectional Reflectance Distribution Function (BRDF) Prediction with Phenotypic Traits in Four Species: Development of a Novel Measuring and Analyzing Framework
Plant Phenomics
Deng, L.; Yu, L. X.; Mao, L.; Wang, Y.; Guo, X.; Wang, M.; Zhang, Y.; Song, Q.; Zhu, X.-G.
Research software2025
Digital Plant Phenotyping Platform (v25.0)
Zenodo
Deng, L.
An integrated platform for plant phenotyping, data processing, and analysis. Core modules have been transferred through Shufeng Bio for applied phenotyping and intelligent-agriculture services.