Synthetic training data and model development for agricultural robotics. Build for the conditions that matter. Measure the result.
Light across a leaf. A change in focus. The small variations that give a scene its character.
We’re developing a workflow for focused model-development projects. Future pilots will have an agreed scope, schedule and evaluation criteria. We’re collecting interest while we improve and validate the models.
Customer model delivery is not yet available. Accuracy, supported hardware and licensing must be established before we offer a handoff.
Eleven plant categories are built, each with a specimen scene and a planted plot, twelve geometries across three growth stages, and twenty-four leaf textures. Detection results exist for Palmer amaranth in cotton, and earlier wheat work tested synthetic-only transfer against a public field benchmark. A library asset is not a validated detector — every new task needs its own feasibility review and evaluation before we offer a model.
Before anyone buys training data, the useful question is where the current model actually fails. Most perception gaps are not a data-volume problem — they are a resolution, size-threshold or stage-coverage problem, and those are measurable. We reproduce your baseline on your own images, measure where it misses, and tell you what would close the gap — including when the answer is that you don’t need us. Both engagements are scoped to the problem; we agree cost and schedule on the call, before any work starts.
An unfavourable result is a completed diagnostic. If the finding is that your existing pipeline is near its ceiling, or that a configuration change gets you most of the way, that is the deliverable and the engagement ends there. We do not hand over the generator itself — you get the model, the data and the evidence. Start a conversation →
Tell us the crop, the camera, and the failure you already know about. If it isn’t a fit, we’ll say so on the first call. Our own models are still being improved and validated; we will show you what they do and do not do before anything is agreed.
From generated scenes to measured results — built so model-development decisions can be checked.
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