nesteye combines computer vision, environmental sensing, and edge inference to turn barn activity into practical operating signals.
The goal is not to show operators more data. It is to identify a meaningful change, preserve its context, and make the result measurable.
nesteye evaluates flock movement across time. Repeated-frame confirmation helps separate a developing pile from normal clustering, changing light, dust, and the visual complexity of a working barn.
A developing cluster must persist across multiple frames before the system treats it as a confirmed event, reducing reactions to ordinary movement.
Vision-derived body measurements support daily weight and uniformity estimates across more of the flock than a small manual sample.
Environmental readings sit on the same timeline as flock behavior so operators can investigate the conditions surrounding a change.
Current disease signals are reviewed with flock behavior and barn context so operators can investigate and respond sooner.
Piling detection scored 0.998 average precision on held-out test data. That is a test result, not a guarantee of performance in every barn. Layout, installation, lighting, dust, flock behavior, and operating conditions can affect results, and pilot planning establishes the correct baseline for each operation.
Disease, mortality, and gait monitoring are current nesteye capabilities. Their signals appear with barn context for operator review, while commercial onboarding establishes barn-specific baselines and validates performance for each operation.
nesteye's feces-based Salmonella model flags visual changes for operator review on the existing camera and edge system. Supporting 2024 research found broiler activity can carry an early infection signal.
The model works as an operator-review signal. It is not a laboratory replacement and does not establish a diagnostic claim.
Source: Poultry Science Association Annual Meeting abstractnesteye translates peer-reviewed poultry research into testable barn methods. Published studies guide the architecture; pilot data determines what the product can responsibly claim.
Temporal behavior
Published work in commercial broiler houses shows that trajectories can be grouped into distinct activity patterns. Our research direction builds on the same principle: persistence, density, and motion change carry more meaning together than any isolated frame.
Daily weight visibility
Depth-based broiler research combines instance segmentation, physical shape features, and learned features before regression. That informs a careful path toward daily flock weight and uniformity estimates without relying on a few convenient birds.
Piling intelligence
Automatic piling research compares temporal and convolutional models because dense clustering develops across video, not in a single still. nesteye uses repeated-frame confirmation to distinguish a developing event from ordinary flock movement.
