4D Plant Digital Twin
A tomato (and nine other crops) growing from emergence to ripening over 120 days as PlantState(t) = f(environment, physiology). Change the light, water, temperature, CO₂ or nutrients on any day: the past stays frozen, the future is recomputed, and two or three futures run side by side from the identical state.
- days, emergence to ripening
- 120
- crops with published parameters
- 10
- parallel futures from one state
- 3
- causes read from trajectories, not scripted
- Derived
The challenge
Growers see the effect of drought or poor light weeks after the cause. Agronomy models exist but are hard to question: why is this leaf smaller, and what if we had irrigated on day 45?
What we built
- Daily physiology step with species parameters from DSSAT CROPGRO genotype files
- Immutable history, recomputed future, structural sharing between branches
- Causal events and explanation chains read from trajectory differences
- Organ geometry generated from the simulated state
Outcome in the twin
Interventions are previewed before they are committed, drought → recovery experiments run in seconds, and every “why” points to a measurable difference between two futures.
Path to production
Drive the model from greenhouse climate sensors and calibrate against measured growth for a specific cultivar.
Technology
About this build
Simplified growth model; a demonstration, not an agronomic decision tool. Engineering report →
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