Why it matters
Without this, decay risks may go unnoticed, leading to unexpected failures, unsafe pruning or removal decisions, and failed audits due to missing decay records and plant health data.
From design to safety
Design
AI informs survey and design decisions
Reads sonic tomography data, resistograph traces, residual wall thickness, and decay extent to guide species selection, work methods, and planning job sequencing. It integrates BS 3998 guidance, checks crown-to-stem balance, and flags potential root protection area conflicts with proposed layout.
- sonic tomograph scanner
- resistograph probe
- drill core samples
- decay extent mapping software
Build
Site setup supports remote sensing integration
Configures data capture workflows, calibrates scanners, and aligns trenching or excavation plans with identified decay zones. Produces a pre-work decay clearance checklist and marks hazard zones on site drawings.
- sonic tomograph scanner
- drill core samples
- field data logger
- timber condition logbook
Operation
On-site assessment and action planning
Continuously reads live data to adapt rigging, pruning, dismantling, or removal strategies based on estimated decay. Generates alternate work orders, short-interval re scans, and a visibility layer for ground staff.
- decay extent mapping software
- residual wall thickness gauge
- field data logger
- timber condition logbook
Maintenance
Ongoing monitoring and data refresh
Schedules re-inspections, updates decay maps, and integrates new resistograph readings to reassess risk. Tracks sensor calibration intervals, data integrity checks, and archival of prior maps for trend analysis.
- decay extent mapping software
- residual wall thickness gauge
- field data logger
- timber condition logbook
Safety
Safeguards and regulatory validation
Ensures readings are signed off by a qualified arborist, aligns with LOLER, BS 3998, and utility clearance; records public protection measures. Maintains a safety file with decay data underpinning permit decisions.
- sonic tomograph scanner
- resistograph probe
- drill core samples
- timber condition logbook
Part by part
- 1
sonic tomograph scanner
- Watch for
- changes in detected wave velocity indicating internal decay
- AI action
- flag anomalies exceeding threshold and trigger decay extent analysis; store timestamps and calibration data in audit trail; export to decay extent maps with geo reference
- 2
resistograph probe
- Watch for
- irregular resistance patterns suggesting hollowing or internal decay
- AI action
- generate decay extent map and severity ranking; apply confidence scores and cross-check with TOMO data; request core depth supplementation if gaps appear
- 3
drill core samples
- Watch for
- reduced residual wall thickness along core segments
- AI action
- mark samples for laboratory confirmation and update decay model; log bore path coordinates and sample IDs for traceability
- 4
decay extent mapping software
- Watch for
- inconsistent integration of scan and drill data
- AI action
- update decay contour visualization and risk zones for work planning; create layer-rich reports including 2D/3D views and cross-sections
- 5
field data logger
- Watch for
- gaps in sensor data or timestamp misalignment
- AI action
- synchronize data streams and timestamp entries for audit trail; flag data gaps and prompt crew to reseal loggers after maintenance
- 6
timber condition logbook
- Watch for
- missing records of inspections and decisions
- AI action
- auto-fill activity log with AI-derived notes and sign-off prompts; attach photos, GPS tags, and job codes
Worked example
Catching a decline in stem integrity before a planned prune
Illustrative scenario with sample data.
A mid-size urban tree shows signs of internal decay; pre-prune AI reads multiple data streams to decide safe work methods, considering utilities and public safety.
Signal in
sonicVelocity: 1680 m/s; resistographDepths: [2,5,9,12] mm; residualWall: [12,11,9] mm; decayExtent: moderate at mid-stem; location: 4 m above grade; borePaths: [id-101, id-102]; project: street tree upgrade
System out
Decay map indicates moderate internal loss; risk zone 60 cm above ground; recommended avoidance of high-tall cuts; plan for reduced-pruning height and follow-up re-inspection; prep work order PR-77 and safety briefing.
Human sign-off: Arboricultural consultant reviews the AI output, confirms decay extent, aligns with BS 5837 constraints, approves alternative pruning strategy and monitoring plan; signs off the work order and informs the utility controller.
How to roll it out
Phase 1
Weeks 1 to 4: Baseline
Install sensors, calibrate devices, and establish data capture workflows; validate models against known decayed samples.
Phase 2
Weeks 5 to 8: Field testing
Run initial assessments on a sample of target trees; adjust thresholds and reporting formats for crews.
Phase 3
Weeks 9 to 12: Full deployment
Scale AI processes across all suitable trees; integrate decay outputs into planning, execution, and safety documentation.
Where people stay in charge
- Decisions affecting safety and work method must be signed off by a qualified arborist or the local authority tree officer.
- Data interpretation thresholds must be validated against standard references (BS 3998, BS 5837) and LOLER compliance before actions.
- Records and reports created by AI must be reviewed, corrected if needed, and archived by human supervisors.
Questions owners and crews ask
How does AI decay detection help with planning jobs and plant health decisions?
AI reads sonic tomograph scans and resistograph traces to estimate decay extent and residual wall thickness, guiding work methods, species considerations, and safety planning, with human sign-off on final decisions.
What data feeds are required for AI to detect decay accurately?
Data from sonic tomograph scans, resistograph traces, residual wall thickness measurements, decay extent estimates, bore path data, and field notes feed the AI model; calibration data ensures accuracy.
Who validates AI-driven decay assessments in practice?
A qualified arborist or the local authority tree officer reviews AI outputs, confirms risk zones, and approves or revises the proposed work plan.
What records are produced to support compliance and audits?
Decay maps, wall thickness trends, inspection logs, field notes, sign-off records, and work method changes are stored for regulatory compliance and audits.