What this means in practice

  • Collect examples from the cameras and conditions the system will actually see.
  • Include the rare and difficult cases, not just typical ones.
  • Start by adapting a pre-trained model; train from scratch only if that clearly falls short.
  • Keep a separate test set the model never trains on, and add new examples when accuracy drops.

How Lid Vizion approaches it

We start with the business decision and operating context, define ownership and measurable milestones, then use configuration, integrations, automation, or custom software only where each earns its place.

See it as a workflow

  • Field scouting from phone and drone images: Turn the photos scouts and drones already capture into a map of possible problem areas, such as weeds, pests, disease or water stress, so agronomists can decide where to look first.