Onboard a new crop in days, not weeks.
Realistic, already-labelled field images of a new crop — so your robot tells weed from crop on roughly 5× fewer real photos.
Precision weeding & spraying — placebo-controlled, validated in your vertical, replicated on a second independent dataset.
Placebo-controlled in precision weeding: synthetic pre-training reaches the same accuracy with roughly 5× fewer labelled images — biggest gains exactly where weed and crop are both green.
The missing photos, already labelled.
Real, already-labelled training images generated by Synthgen: new plants placed into the field at true scale, grounded and lit to sit naturally — every one auto-labelled weed or crop as it's made. No hand-annotation.
Source imagery: WE3DS crop/weed dataset (Kicherer et al.), CC BY 4.0. Synthetic images + labels by Synthgen.
The crop-onboarding benchmark.
Send us your next crop. We build synthetic pre-training data from your own field imagery — auto-labelled, pixel-exact — then benchmark it against your real-only baseline on your own fields, under placebo controls so the lift is real. You name the bar before we start (say: today’s accuracy at a fraction of the labels), and you pay only if we clear it.
Pay on results — you set the bar, we clear it or you don’t pay. No free pilots, no free work: just a measured answer.
The old way, retired.
Measured, not promised.
fewer labelled images to reach the same accuracy on a new crop — ~10 instead of ~50.
on green-on-green weeds at deployment operating points — the case that decides row work.
crop plants wrongly hit at matched weed recall in our validation — vs 4.4% for the baseline. The safety axis no one else measures.
Placebo-controlled on real held-out field images in our precision-weeding validation — and replicated on a second, independent field-robotics dataset, where 15 labelled images plus synthetic pre-training took a weed detector from unusable to working. Your number comes from your own fields.
Edge-ready: with synthetic pre-training, a nano model at 640 px matched a real-only model at 1280 px in our validation — headroom your onboard compute will feel.
Honest in both directions.
Placebo-controlled, always.
Anyone can show a lift that’s really just more data. Every claim we make has survived placebo-controlled benchmarks — if the gain isn’t real, we don’t publish it.
The safety axis.
A weed left standing costs a pass; a crop plant burned costs the row. We measure crop plants wrongly hit at matched recall and report it next to the accuracy number.
Your fields, not a render farm.
Built on your own field imagery, with pixel-exact labels — faithful to how your crops and weeds actually look, not stock photos or guesswork.
Two weeks to the answer — then days per crop.
You send field photos of the new crop, with labels for a small starter set, and we agree the class map.
~1–2 hrs of your engineer
A short fit check on your imagery and classes — we only run benchmarks we expect to win.
a short call
We generate the synthetic training data from your imagery, pre-train, fine-tune, and benchmark it under placebo controls.
nothing from you
A measured answer on your held-out field images: accuracy, label count, and the safety readout.
30-min readout
A win becomes a pilot — a one-month engagement at minimum — then every new crop, region, or season onboarded the same way.
you decide
Send us your next crop.
One benchmark. Your fields. Your number.
Private & isolated · your images are never shared or reused · synthgen.co
Agricultural results are detection results from our precision-weeding validation — placebo-controlled on real held-out field images; crop-safety measured at matched weed recall. Results are per crop and produced on your own data, against a bar you set before we start.