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Curriculum Vitae

· 6 min read
Liangchao Deng
Liangchao Deng
PhD @ SHZU

Personal Information​

Liangchao Deng (邓良超) <SpeechButton text="邓良超" fallbackText="Pronunciation: Liáng-chāo (lee-ANG chao)" /> Ph.D. Candidate in Crop Science College of Agriculture, Shihezi University Shihezi, Xinjiang 832003, China

Contact Information:

Academic Profiles:

Education​

Ph.D. in Crop Science | 2021 – PresentShihezi University, Xinjiang, China

  • Supervisors: Prof. Yali Zhang & Dr. Qingfeng Song
  • Dissertation Topic: AI-Enhanced High-Throughput Phenotyping and 3D Canopy Modeling for Precision Agriculture
  • Expected Graduation: 2026

B.Sc. in Information and Computational Science | 2016 – 2021Shihezi University, Xinjiang, China

  • Thesis: Numerical Methods for Agricultural Data Analysis

Research Interests​

Cutting-Edge Technology Focus​

  • Generative AI for Agriculture: Image-to-3D plant modeling, foundation models for crop phenotyping, multimodal systems for agricultural applications
  • Advanced Computer Vision: Neural radiance fields (NeRF), 3D Gaussian splatting, differentiable rendering for plant reconstruction
  • AI-Assisted Scientific Computing: Large language model integration in research workflows, ai code generation for phenotyping pipelines
  • Digital Agriculture Innovation: IoT sensor networks, edge computing for real-time crop monitoring, blockchain for agricultural traceability

Core Research Domains​

  • Precision Phenomics: High-throughput plant phenotyping using UAV swarms, hyperspectral imaging, and LiDAR point clouds
  • Computational Plant Biology: Ray-tracing photosynthesis models, BRDF-based optical simulations, digital twin ecosystems
  • Environment -Smart Agriculture: AI-driven crop adaptation strategies, predictive modeling for environment resilience

Innovation and Impact​

  • Bridging the gap between cutting-edge AI research and practical agricultural solutions
  • Developing scalable technologies for global food security challenges
  • Creating open-source tools for the international agricultural research community

Research Experience​

Generative AI for Agricultural Applications | 2024 – Present​

Advanced Ph.D. Research, Shihezi University

  • Innovation: Pioneered Image-to-3D generative models for rapid plant architecture synthesis using diffusion models and neural radiance fields
  • AI Integration: Fine-tuned large language models for domain-specific agricultural data analysis and automated research workflows
  • Technical Achievement: Developed novel AI-assisted coding framework that accelerated phenotyping algorithm development
  • Impact: Reduced 3D plant model generation from days to minutes, enabling real-time digital twin applications

Next-Generation Phenotyping with Advanced Computer Vision | 2021 – Present​

Collaborative Research with International Partners

  • Cutting-Edge Methods: Implemented 3D Gaussian splatting and differentiable rendering for photorealistic plant reconstruction
  • Scale Innovation: Developed distributed computing pipeline processing UAV imagery using cloud-native architectures
  • Real-World Impact: Technology adopted by agricultural research institutions for breeding programs
  • Funding: Contributed to $500K research grant proposal (pending)

Technical Expertise​

Artificial Intelligence & Machine Learning​

  • Deep Learning: PyTorch, TensorFlow, JAX, Hugging Face Transformers, CUDA programming
  • Generative AI: Diffusion models, GANs, NeRF, 3D Gaussian splatting, text-to-3D synthesis
  • Foundation Models: LLM fine-tuning (LoRA, QLoRA), multimodal models (CLIP, DALL-E), prompt engineering
  • MLOps: Docker, Kubernetes, MLflow, Weights & Biases, distributed training on multi-GPU clusters

Advanced Computer Vision & Graphics​

  • 3D Reconstruction: Structure-from-Motion, multi-view stereo, photogrammetry, point cloud processing
  • Rendering: Ray tracing, path tracing, differentiable rendering, physically-based rendering (PBR)
  • Real-time Processing: OpenCV, CUDA, TensorRT, edge deployment on NVIDIA Jetson
  • Specialized Libraries: Open3D, PCL, Blender Python API, Three.js for web visualization

High-Performance Computing & Cloud​

  • Programming: Python, Matlab, AI vibe coding
  • DevOps: Git,, containerization, infrastructure as code (Terraform)

Agricultural Technology & IoT​

  • Remote Sensing: Hyperspectral/multispectral imaging, LiDAR, thermal imaging, UAV systems
  • Sensor Networks: IoT device programming, edge computing, real-time data streaming
  • Precision Agriculture: Variable rate technology, GPS/GNSS, agricultural robotics
  • Data Standards: GeoTIFF, NetCDF, agricultural data exchange formats

Research & Development Tools​

  • Scientific Computing: MATLAB, R, Jupyter notebooks, scientific visualization
  • Collaboration: GitHub, Slack, Notion, academic writing tools (LaTeX, Overleaf)
  • Project Management: Agile methodologies, Scrum, research project coordination

Publications & Research Output​

High-Impact Manuscripts in Preparation​

  1. •Deng,L., Yu, L. X., Mao, L., Wang, Y., Guo, X., Wang, M., Zhang, Y., Song, Q., Zhu,X-G. (2025). Leaf Optical Properties Predicted with BRDF and Phenotypic Traits in Four Species: Development of Novel Analysis Tools. (Plant phenomics, Under major revision)

Teaching and Mentoring Experience​

Teaching Assistance​

  • [List courses assisted with]

Student Mentoring​

  • Yu Jingxuan | Undergraduate Research Assistant | 2023-2024Project: "Field-Scale 3D Reconstruction and Quantitative Analysis of Cotton Varieties"Supervision: Guided development of multi-view stereo reconstruction pipeline for cotton phenotypingOutcome: Student gained proficiency in 3D modeling and agricultural data analysis
  • Zhang Rongze | Undergraduate Research Assistant | 2023-2024Project: "Individual Cotton Plant 3D Reconstruction and Morphological Quantification"Supervision: Mentored in computer vision techniques and plant architecture analysisOutcome: Contributed to automated phenotyping workflow development
  • Xie Hejiang | Undergraduate Research Assistant | 2022-2023 Project: "Cotton Yield Response Analysis Under Different Nitrogen Treatment Regimes" Supervision: Trained in experimental design, statistical analysis, and agricultural data interpretation Outcome: Results contributed to nitrogen optimization research for sustainable cotton production

Mentoring Philosophy: Emphasize hands-on learning, technical skill development, and integration of computational methods with agricultural research applications.

Innovation & Entrepreneurship​

Open Source Contributions​

  • Stereo-Vision-Camera-Box | Python, Computer VisionAdvanced stereo vision system with custom hardware integration and GUI interface for high-precision depth measurement and 3D point cloud generation. Features real-time processing capabilities for agricultural phenotyping applications.
  • CCO-Flight-Planner | Python, UAV TechnologyAutomated flight planning tool for DJI drones with KML polygon input and KMZ waypoint generation. Streamlines UAV-based agricultural surveys and remote sensing data collection workflows.
  • RootQuantify | Python, Image AnalysisSpecialized batch processing toolkit for quantitative analysis of plant root system images. Implements advanced image processing algorithms for root architecture phenotyping and morphological measurements.
  • Custom-Harvard-Citation-Tool | Academic Productivity Zotero integration tool for automated citation insertion in presentations with journal abbreviation support. Enhances academic workflow efficiency for research presentations and publications.

Technical Impact: Combined 50+ stars across repositories, demonstrating community adoption and practical utility in agricultural research workflows.

Languages​

  • Chinese (Mandarin): Native speaker
  • English: Proficient (TOEFL/IELTS score if available)
    • Academic writing and presentation
    • Scientific communication
    • International collaboration

References​

Prof. Yali Zhang Professor and Ph.D. Supervisor College of Agriculture, Shihezi University Email: zhangyali_cn@foxmail.com

Dr. Qingfeng Song Research Scientist and Co-supervisor CAS Center for Excellence in Molecular Plant Sciences Email: songqf@cemps.ac.cn

Additional references available upon request

Last updated: September 2025