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Deer Intel

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Thermal survey, cull, TB, and tick-borne disease monitoring

Trooperstown and Lickeen Deer Project

Observed deer 0 Adjusted detailed survey count
Estimated population 0 Local model estimate
Density 0.0 Observed deer per km²
Estimated density 0.0 Modelled deer per km²
Cull records 0 Animals logged with health data
Disease positives 0 TB or tick-borne alerts
Impact score 0.0 Average habitat/crop pressure

Activity Timeline

Monthly observations, culls, impact assessments, and lab results.

Health Watch

Sample results requiring attention.

Recent Records

Latest field and lab activity.

Briefing Spine

Committee-facing narrative from the presentation deck.

Project Context

Extracted from the supplied project material.

1,500 acresExpanded project area
149 thermalDeer recorded in March 2026
20 tested so far3 TB and 3 non-TB Mycobacterium
UCD / OH-ALLIESTick-borne disease research
Date Type Site Method Count Health / Impact GPS

Project Map

GPS points plotted from all field and lab records.

Sika-focused management logic

Control success depends on removing breeding females from cover-heavy habitat.

For this project area, the management framework should treat Sika / Sika-like deer as the primary operating case: hard to detect, more nocturnal under disturbance, cover-oriented, and capable of high annual increase in Wicklow conditions.

Direct Observation 16

Initial early-morning count used as the first formal survey baseline.

Revised Count 92

Continued monitoring under Section 42 showed the first estimate was too low.

Ground Thermal Survey 149

March 2026 on-the-ground thermal team survey total after removing 45 enclosed deer from 194 locations.

Density 0.0 / km²

Based on adjusted observed deer over the expanded project area.

Cull Progress 66 / 69

Current group cull progress against the expanded-area cull plan.

Observed Deer And Cull Plan

Local project logic based on uploaded year-one survey and cull plan.

Species Management Comparison

Sika is the operating priority for this project area.

Feature Sika Red deer Fallow deer

Trooperstown and Lickeen Deer Project

Operational Summary

Observed0
Est. population0
Observed density0.0
Est. density0.0
Culled0
Samples0
Disease positives0
Impact0.0

Dashboard Metrics

Management Narrative

Disease Surveillance

Sika Management Logic

Presentation Outline

    Year-one Evidence Spine

    Population Model Table

    Category Aug Yr 1 Cull Post-cull Mortality Aug Yr 6

    DJI Matrice 4TD workflow

    AI-assisted thermal sightings

    Matrice 4TD detections should enter the database as draft sightings with GPS, timestamp, thermal evidence, confidence score, and reviewer status before they affect management decisions.

    Integration Path

    Start with import and review, then connect live DJI/FlightHub events when the field workflow is proven.

    1 Capture

    Matrice 4TD records RGB/thermal media, aircraft GPS, altitude, camera angle, and timestamp during repeatable survey flights.

    2 AI Detect

    Cloud or onboard detection flags likely deer heat signatures and creates draft observations with confidence values.

    3 Review

    A human reviewer confirms, rejects, or adjusts counts to avoid double-counting and false positives.

    4 Record

    Confirmed sightings become normal database records for maps, cull planning, reports, and presentations.

    Matrice 4TD Fields

    Recommended fields for automated drone detections.

    Detection ID Flight ID Survey site Date and time Latitude / longitude Altitude Thermal frame RGB frame AI count Confidence Reviewer status Notes

    Current Drone Records

    Confirmed drone thermal observations already in this database.

    Import Template

    Use this structure for AI detections exported from FlightHub, a DJI bridge app, or offline thermal analysis.

    {
  "platform": "DJI Matrice 4TD",
  "flightId": "M4TD-2026-03-01-001",
  "site": "Site 4 - Trooperstown North",
  "detections": [
    {
      "timestamp": "2026-03-01T05:42:00Z",
      "lat": 52.99965,
      "lng": -6.24809,
      "count": 3,
      "confidence": 0.86,
      "thermalImage": "thermal-frame-0042.jpg",
      "reviewStatus": "draft"
    }
  ]
}

    Matrice 4TD integration instructions

    Connect drone evidence to confirmed deer records.

    The website should receive reviewed Matrice 4TD detections from DJI FlightHub 2, a DJI bridge app, or an offline AI analysis workflow. The browser app stores and reports the data; drone control and live AI should sit in the DJI layer.

    Recommended Setup

    Use this route first because it protects data quality and avoids counting the same deer twice.

    1 Plan repeatable flights

    Create named routes for each survey block, including site, flight ID, date, start time, weather, pilot, and survey objective.

    2 Capture thermal and RGB evidence

    Record thermal frames, RGB stills/video, aircraft GPS, altitude, gimbal angle, timestamp, and camera mode for each candidate sighting.

    3 Run AI detection

    Use FlightHub 2 third-party algorithms, an onboard/cloud compute service, or offline thermal image analysis to create draft detections.

    4 Review before import

    Confirm deer, remove duplicates, correct counts, and reject livestock, humans, warm rocks, machinery, or repeated detections from overlapping passes.

    5 Import confirmed records

    Bring only reviewed detections into the deer database as observation records with method set to Drone thermal.

    6 Use density and cull logic

    Use the adjusted detailed survey count for headline density and management planning, not the sum of every detection row.

    Minimum Import Fields

    These fields should be required for every confirmed drone detection.

    • Flight ID and survey site
    • Date and precise timestamp
    • Latitude, longitude, altitude, and coordinate source
    • AI count, confidence score, and reviewer status
    • Thermal frame or video reference
    • RGB frame or supporting image where available
    • Reviewer notes on double-counting risk

    Data Quality Rules

    Use these before a detection becomes part of the official project count.

    • Do not count unreviewed AI detections as confirmed deer.
    • Keep raw detections separate from adjusted survey totals.
    • Mark overlapping flight passes and likely duplicate animals.
    • Use the most detailed survey as the headline observed count.
    • Store evidence links so each record can be audited later.

    Connection Options

    Choose the integration level according to budget, connectivity, and DJI account setup.

    Option Best use How it feeds the website
    Offline import First working version and field validation. Upload reviewed CSV/JSON detections after each flight.
    FlightHub 2 cloud algorithm Near-live AI alerts and central mission management. Send reviewed detection events through an API bridge.
    Custom DJI bridge app Direct tablet workflow during flights. Use DJI SDK data to create draft sightings for review.
    Onboard/payload compute Low-connectivity or specialist live processing. Export detections with GPS/time/evidence after landing or sync when online.

    New database entry

    Add field, cull, impact, or lab data

    On mobile, allow location access to automatically log the current GPS point.
    Deer impact assessment
    Repeatable site assessment Record impact evidence, not deer numbers alone. Use the same site, GPS point, photo angle, and scoring logic on each revisit.
    Habitat condition Vegetation response and recovery indicators
    Damage evidence Physical signs used to guide targeted management
    Overall impact: 0 - None observed Overall score uses the highest evidence score recorded in this visit.

    New management area

    Set up a separate project

    Drone flight data

    Use a boundary polygon where possible. A raw flight track can estimate the surveyed footprint, but reviewed project boundaries are better for density calculations.