Why it matters
Without AI, misaligned gangs cause uneven pluck cycles, overburdened workers, delayed leaf transport, and inconsistent leaf quality leading to reduced auction valuations and risk of MRL issues.
From design to safety
Design
AI-guided layout for plucking chapters
Readings include section boundaries, yields by section, gang size caps, leaf maturity indices, terrain slope, access routes, drone imagery, sun exposure, soil compaction, and microclimate to propose allocations.
- Section map tablet
- Labour forecasting dashboard
- Plucking gang roster
Build
Install data collection for sections
Install leaf maturity sensors, section cameras, worker badge readers, route beacons, drone locator tags, calibration schedules, time sync, data quality checks, and fiber backhaul to seed allocation algorithms.
- Leaf maturity sensor
- Section boundary beacon
- Worker badge reader
- Leaf transport skiff
Operation
Dynamic rounds and route optimization
AI reads rounds, transport times, wither levels, processing capacity, congestion heatmaps, fatigue scores from wearables, yields per section, and seasonal leaf quality variation to adjust allocations in real time.
- Leaf receiver tray
- Rotary transport skiff
- Plucking scissor set
Maintenance
Recalibrate after pruning and season shifts
Periodically retrain on new harvests, prune cycles, soil changes, section boundary updates, drift logs; apply drift correction, feature scaling, model versioning, change logs, governance checks, and audit trails.
- Section map tablet
- Labour forecasting dashboard
Safety
Workload fairness and compliance checks
Monitor fatigue, break compliance, PPE usage, incident logs, spray residue checks, certification training, heat stress monitoring, incident investigation workflow, and audit trails to preserve welfare and regulatory readiness.
- Worker badge reader
- Safety inspection tablet
Part by part
- 1
Leaf receiver tray
- Watch for
- Wear on clamp interface or misalignment causing leaf loss during harvesting.
- AI action
- Detect packing misalignment and trigger re-seating and calibration of blades.
- 2
Plucking scissor set
- Watch for
- Blade dullness or frame flex reducing leaf cleanliness during rounds.
- AI action
- Alert maintenance to replace blades or adjust tension in time.
- 3
Leaf transport skiff
- Watch for
- Jams on gentle incline, uneven leaf flow causing delays during transit.
- AI action
- Re-route sections to prevent crowding and optimize flow through narrow junctures.
- 4
Section map tablet
- Watch for
- Section boundary drift or mislabeling of plots in recent survey.
- AI action
- Flag boundary drift and prompt re-synchronization with field team notes.
- 5
Labour forecasting dashboard
- Watch for
- Forecast error in expected labour by section for the coming rounds.
- AI action
- Adjust gang allocations and overtime flags to align with risk buffers.
- 6
Plucking gang roster
- Watch for
- Mismatch between skill level and section demand during peak rounds.
- AI action
- Suggest re-assignment based on skill and historical yield patterns.
Worked example
Catching a section drift before a misallocation
Illustrative scenario with sample data.
The system detects a drift in section boundaries due to recent pruning and reallocates gangs to maintain even leaf maturity.
Signal in
season: 2026-2027, estate: NorthVale (120 ha), factory output: 4200 kg made tea/season, sections: 24, pruning cycle: 4 months, gangs: 6, dataQuality: true, roundsPerWeek: 3, planningWindow: 2
System out
Alert sent to estate manager with revised section map, adjusted gang allocations, and workload distribution for the next two rounds.
Human sign-off: Estate manager reviews and approves the reallocation, then factory supervisor approves the updated route and section plan.
How to roll it out
Phase 1
Weeks 1 to 4
Baseline data collection and model bootstrapping with existing section maps and gang rosters.
Phase 2
Weeks 5 to 8
Pilot allocation in two harvest rounds, calibrating maturity indices and route efficiency.
Phase 3
Weeks 9 to 12
Full rollout with adaptive scheduling and continuous learning from real rounds.
Where people stay in charge
- Allocations must be reviewed by the estate manager and field supervisor to ensure safety and compliance with labour laws.
- Safety constraints such as maximum daily hours and mandatory breaks must not be overridden without sign-off by the safety officer.
- Any algorithm changes that affect harvest plans require an audit trail and governance approval before deployment.
Questions owners and crews ask
How will AI decide which plucking gang works a given section?
AI combines maturity scores, section yield history, worker capability profiles, current weather, terrain, fuel or transport time, and route efficiency to assign gangs, updating in real time with auditable logs for supervisor review.
What data is needed to start AI plucking allocation?
Section maps, leaf maturity sensors, pruning records, worker rosters, transport times, and historical harvest data form the baseline for initial allocation decisions.
Who signs off on changes to labour allocation?
The estate manager or designated field supervisor signs off, with safety officer and certification records attached, then factory supervisor approves route plans.
How is worker safety protected in the system?
Fatigue and break rules are monitored via wearables, PPE checks logged, incident reports trigger alerts, and corrective actions are recorded for compliance audits.