Why it matters
Without this, mis-timed cuts cause over- or under-harvesting, increased leaf damage, inconsistent withering and fermentation, reduced made tea outturn, and higher risk during audits and certifications.
From design to safety
Design
AI-guided harvest design
Data reads from terrain maps, shade, soil moisture, drainage lines, and existing harvester specs to set optimal cut height, blade angle, row spacing, and scissor kinematics for the fleet, with tolerances aligned to two leaves and a bud targets.
- Harvester header
- Cutting blade
- Height sensor
- Leaf damage sensor
Build
Install and calibrate harvesting AI
Integrator uploads calibration curves, tests leaf sampling, and logs initial yield targets and damage baselines from prototype trials, including field-verified roughness, slope, and vibration profiles for reliable operation.
- Height sensor
- Cutting blade
- Leaf damage sensor
- Yield gauge
Operation
Real-time harvest control
System reads plucking rounds, leaf transport speed, damage indices, and blade wear to adjust blade force, cut height, suction, and vibration dampening for efficient collection and minimal tissue rupture.
- Harvester header
- Conveyor belt
- Leaf collection chute
- Blade servo motor
Maintenance
Ongoing calibration and wear checks
Periodic recalibration of sensors, blade wear monitoring, rotor balance checks, and drift analysis to maintain consistent cut quality, reduce leaf damage, and extend component life.
- Blade wear sensor
- Blade servo motor
- Conveyor belt
- Harvester frame
Safety
Harvester safety and compliance
Monitoring flags overloads, PPE adherence, residue checks, and battery health, with automatic stop requests to the responsible supervisor and log for audits and regulatory reviews.
- Emergency stop module
- PPE sensor
- Residue monitor
- Control safety interlocks
Part by part
- 1
Harvester header
- Watch for
- Unstable cut heights causing uneven leaf size and wasted buds.
- AI action
- Adjust cutting height and blade angle to stabilize leaf size and reduce buds damage.
- 2
Cutting blade
- Watch for
- Accelerated wear leading to tearing or crushing of leaf.
- AI action
- Trigger blade sharpening or replacement request; adjust blade motor torque.
- 3
Height sensor
- Watch for
- Drift in sensor reading due to vibration or bogie misalignment.
- AI action
- Recalibrate height reading and notify maintenance if drift exceeds threshold.
- 4
Leaf damage sensor
- Watch for
- Excessive micro-damage on two-leaves-and-a-bud
- AI action
- Reduce feed rate and adjust blade contact pressure to minimize damage.
- 5
Yield gauge
- Watch for
- Anomalous drop in inferred yield per hectare
- AI action
- Flag potential under-harvest zones and replan passes for that block.
- 6
Conveyor belt
- Watch for
- Slippage or jams in leaf transport
- AI action
- Pause line, clear jam, re-synchronize feed rate with harvester cadence.
Worked example
Catching a blade wear early before a harvest window
Illustrative scenario with sample data.
A mid-season deploy reveals blade wear signaling potential leaf damage risk if not addressed before next round.
Signal in
harvester_id=H-Alpha, blade_wear_pct=12, cut_height_mm=26, leaf_size_avg=18mm, harvest_round=R3, temp_C=28
System out
alert_type=warning, blade_wear_pct=12, action=retune_and_schedule_sharpening, recommended_cut_height=27
Human sign-off: Field supervisor approves sharpening and adjusts cut height before next round.
How to roll it out
Phase 1
Weeks 1 to 2
Baseline deployment, sensor calibration, and initial threshold setting for cut height, yield estimates, and leaf-dreshing tolerances.
Phase 2
Weeks 3 to 6
Pilot blocks tested, real-time alerts tuned, maintenance cadence established, safety checks documented, and data governance rules enacted.
Phase 3
Weeks 7 to 12
Full-scale operation with integrated QA reports, AI-curated harvest logs, and audit-ready datasets, including traceability from field to made tea.
Where people stay in charge
- Decisions on harvest height and blade settings must stay with the field manager and safety officer when implementing in active rounds.
- All safety-related AI actions must be endorsed by the designated safety steward and logged in the safety register.
- Final adjustments to machine parameters require approval in the weekly operations review and sign-off from estate management.
Questions owners and crews ask
What data does the AI read from the harvester during operation?
The AI reads blade status, cut height, leaf damage indices, harvest rate, transport speed, soil moisture proxies, and sensor temperatures to determine when to adjust cutting parameters and to trigger maintenance tasks.
How is leaf damage minimized by AI during plucking rounds?
The system detects early tissue damage signals and reduces contact force or adjusts feed rate to protect two leaves and a bud, while maintaining throughput within safety limits.
Who signs off on safety-related AI actions in the field?
The field manager and safety officer jointly sign off on automatic stop commands and safe-operate procedures during rounds, with incident logging requirements.
How are maintenance decisions recorded for audits?
All calibrations, wear events, actions taken, and approvals are logged in the maintenance registry with timestamps, operator IDs, and asset IDs for traceability.