Research architecture
From sensing crops to designing crops.
A four-layer architecture for crop intelligence — digitize the physical crop, understand its mechanisms, predict its future, and design what it should become.
What is the crop's state right now?
Multi-view 3D reconstruction, UAV imaging, and computer vision turn a real crop into point clouds and quantified traits — a measurable digital twin.
Sense · Reconstruct · QuantifyWhy does the crop behave this way?
Coupling structure–radiation–photosynthesis–growth processes with scientific AI turns the digital crop into an interpretable, mechanistic model.
Model · Explain · ConnectWhat will happen next?
State-transition dynamics and data assimilation project growth under environment × management scenarios, with decision risk quantified.
Simulate · Forecast · Quantify riskWhat should the crop become?
Inverse design and optimization over genotype × environment × management propose canopy and breeding targets — closing the loop back to the field.
Optimize · Design · DecideDigitize → Understand → Predict → Design → Validate → back to Digitize
Featured tools
Interactive computing, built to run in your browser.
Launch these crop analyses, field data capture, and scientific visualization tools directly.
Sensor Recorder
Capture device orientation, solar geometry, and GPS coordinates for leaf field measurements.
AI Data Visualizer
Upload and analyze crop datasets interactively with publication-ready scientific plotting.
Root Preprocessor
Clean, segment, and preprocess root system imagery to extract morphology phenotypes.
Work together
Have a crop-science problem worth making computable?
I welcome research exchange, open-source collaboration, and carefully scoped commercial projects.
