Livestock computer vision platform

Camera-based cattle monitoring built for real livestock operations.

Fixed color and thermal cameras convert pixels, geometry, and time into reviewable observations for cattle inventory, movement, behavior, identity, and research.

Discuss a deploymentSee what we monitor
Aerial view of cattle pens and monitoring infrastructure at the Tuskegee University deployment

Field deployment

Tuskegee University research feedlot

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Field-deployed foundation

Camera, connectivity, and evidence-capture infrastructure operating in a livestock research environment.

Configured per site

Commercial workflow shaped around the facility, camera views, operating records, and agreed success criteria.

Active R&D

Research capabilities are available to scope with field infrastructure and study partners.

Stewardship made practical

Better awareness supports responsible care.

Proverbs 27:23–24 calls for diligent attention to flocks and herds. That principle guides how we build: preserve useful observations, make the evidence reviewable, and keep the person responsible for the animals in control of the decision.

Faith explains why we care. Field evidence shows how each deployment works.

Cameras, measurements, and site-specific evaluation help responsible teams see more clearly and act with better information. They support human stewardship; they do not replace it.

Read our mission and values

How the platform works

How cameras turn pixels into reviewable cattle observations.

Every deployment begins with an operating or research question. Camera placement, computer-vision models, geometry, time windows, rules, retention, and integrations are then configured around the answer that team needs.

  1. Field-installed camera view of cattle gathered along a feed bunk at Tuskegee University
    Documented field camera view
    01Capture

    Cameras observe the operation

    Fixed color and thermal cameras preserve time-stamped views of pens, gates, bunks, alleys, and the chute.

  2. Computer-vision output showing detected cattle in an overhead pen view
    Computer-vision detection output
    02Measure

    Software turns pixels into observations

    Computer-vision models, geometry, time, and thermal values organize detections into count, presence, movement, and reference observations.

  3. Review requestedPen 4 · 6:20 AM

    Observed count differs from the yard record

    Expected

    60

    Observed

    Review

    Illustrative interface—not a reported field result.

    03Compare

    Site rules surface what changed

    Expected records and observed activity are compared so the team can focus on a pen mismatch, movement event, or identity review.

  4. Source evidence ready

    Review the moment, then choose the action.

    1. 1Open the source frame or clip
    2. 2Compare with the operating record
    3. 3Confirm, correct, or investigate

    Illustrative workflow; deployment outputs are scoped to the site or study.

    04Decide

    A person reviews the source evidence

    The operator or researcher sees the relevant time, location, and source imagery before deciding what happens next.

Direct observations come from captured imagery and time. Counts, movement exceptions, identity associations, and study measures are produced through deployment-specific models, geometry, rules, and validation.

One platform, clear product status

Technology modules shaped around a real job.

Commercial workflows and research modules share the same field-built foundation: RGB and thermal capture, edge compute, solar and wireless infrastructure, and local storage.

Configured per site

Feedlot inventory evidence

Compare expected inventory with camera-observed pen presence and review the evidence behind a possible mismatch.

Explore feedlot workflows

Configured per site

Movement exceptions

Use gate, alley, transfer, and chute coverage to investigate when an animal appears in the wrong place or a movement record needs support.

See movement signals

Field-deployed foundation

Continuous pen monitoring

Maintain a visual record of cattle presence, activity, and zone use across the periods the camera system is configured to cover.

See what we monitor

Active R&D

Radar research capability

Radar is an active R&D and research capability that can be scoped with the field platform for livestock operations.

Explore research partnerships

Active chute workflow

Identity at the Chute

Capture face, muzzle, and ear-tag reference views during controlled processing, then validate candidate matches against known identities.

Review the Nose ID workflow

Configured per study

Research data infrastructure

Define study-specific zones, observations, exports, and review protocols for animal-science and precision-livestock research.

Explore research partnerships

Active research study

RGB + thermal wound review

Synchronized RGB and thermal imagery support a wound-review study and review workflows for livestock research.

See a research application
Solar-powered monitoring node installed beside cattle handling equipment at Tuskegee University
Field-installed camera, power, and connectivity infrastructure at the Tuskegee University deployment.

Built for livestock environments

The hardware is part of the system.

Useful livestock AI starts with dependable views. We assess sightlines, dust, weather, night coverage, power, connectivity, handling flow, and the records a facility already trusts before deciding where intelligence belongs.

Coverage design

Cameras are placed around the operational question, not installed as generic surveillance.

Edge compute and local storage

Core processing and storage run on site with the field platform.

Evidence retention

Relevant frames and clips stay connected to the observation that prompted review.

Livestock workflow integration

The platform connects reviewable evidence with EID/RFID and operating records.

Identity at the Chute

Identity at the Chute

Face, muzzle, and ear-tag reference capture happens while cattle are already being processed. Segmentation organizes the chute imagery into usable identity references connected to known tags and manifests.

Face, muzzle, and tag capture

Collect reference imagery at the chute.

Segmentation

Organize usable muzzle-region references from chute imagery.

Matching evaluation

Matching performance is evaluated by protocol.

Matching performance is evaluated by protocol.

Thermal camera view of cattle lined along a feed bunk at night
Thermal imagery preserves useful surface and presence evidence at night as a complementary observation channel.

RGB + thermal research

The screwworm and wound-review study uses synchronized RGB and thermal imagery to support review of visible wound-like regions and surface-temperature patterns. Veterinary diagnosis and response remain with animal-health professionals.

Review the RGB + thermal approach

Choose the right path

One evidence platform. Two focused conversations.

Feedlot teams can begin with inventory and movement exceptions. Researchers can define study variables, validation methods, and data outputs.

Commercial feedlots

Start with inventory evidence

See how camera observations support pen-count reconciliation and movement review.

Explore feedlot deployment

Research institutions

Design a measurable study

Build around species, variables, validation, evidence retention, and export requirements.

Explore research partnerships
Talk with Steve about your facility