Digital Field Twin
A physics-informed decision engine for crop genetics-×-environment trial analytics — benchmarked on real public research data and gated so it abstains rather than guesses when the evidence isn't there. The decision layer that tells you when it can't predict.
What it is
Breeding programs must decide which untested entries to advance and which site-years run high or low risk — but field phenotyping is expensive and sparse, so most candidate genotypes and environments are never directly measured. The Digital Field Twin composes four evidence-gated modules — Genetics (genomic prediction of untested germplasm), Weather/Environment (environment yield level and stress context), Imagery/EO (satellite & UAV yield signal), and Agronomy (treatment response) — over real public datasets: Genomes-to-Fields maize, SoyNAM soybean, CIMMYT wheat, NASA POWER weather, and more.
Every claim is consolidated into a machine-generated scorecard with an explicit confidence type and a plain-English decision meaning — and a selective-prediction layer that declines to predict outside its support.
Why it's different
Breeding platforms (Phenome Networks), imagery vendors (Hiphen, Solvi, Sentera), and yield-forecast engines (Cropin, Pattern Ag) all sell a confident predictor or a data-management system. None publicly ships an evidence-gated scorecard that publishes its own negative results, or an explicit abstention layer. The Digital Field Twin does both — it separates value by axis (genetics ranks untested germplasm; weather sets the environment level; they compose rather than compete) and reports the 14 things that don't work alongside the 14 that do.
Prepared demo
The evidence scorecard — negatives included.
Every product claim carries a status and a confidence type. Publishing the not-supported findings alongside the supported ones is the point, not an omission.
41 findings from leakage-controlled splits on public datasets. A distribution of evidence, not a percent-complete score — a new module can add both supported and not-supported rows while genuinely improving the system.
Start here
Run a Measurement Validation Sprint on your trial data.
A fixed-scope, 2–3 week engagement on your archived trial data with a pre-registered acceptance test and an auditable go / no-go. The honest way to find out if this helps your program.