Why it matters
Without AI, soft spots, mould, and odour can go unnoticed in storage, leading to degraded truffles, rejected consignments, and costly recalls.
From design to safety
Design
AI-informed storage layout planning and QA mapping
AI analyzes predicted storage volumes, batch diversity, airflow, and zone risk to propose sensor placements, bin zoning, packing line interfaces, and QA checkpoints that support rot prevention and end-to-end traceability.
- refrigerated storage room
- grading conveyor belt
- digital LIMS tablet
Build
Install sensors, cameras, and data pipelines
Deploy high-definition cameras, gas detectors, humidity sensors, temperature probes, wireless gateways, and a secure data lake; establish baseline readings and metadata schemas for future anomaly detection and audit trails.
- refrigerated storage room
- grading conveyor belt
- gas sensor array
Operation
Real-time spoilage monitoring and adaptive grading
Continuous readings from temperature, humidity, VOCs, image analytics, and weight data feed AI models that classify rot risk, trigger grading re-evaluations, and reorganize lots for packing.
- grading conveyor belt
- handheld aroma detector unit
- perimeter air sensor array
Maintenance
Calibration, hygiene, and data quality regime
Scheduled sensor calibration, filter changes, camera recalibration, cleaning of packaging lines, and data quality checks to maintain anomaly detection and repeatable grading outcomes.
- perimeter air sensor array
- handheld aroma detector unit
- digital LIMS tablet
Safety
Traceability, sign-off, and export readiness
AI-generated lot-level grading and removals are signed off by the post-harvest supervisor and food safety authority to ensure export compliance and authentic lot documentation.
- digital LIMS tablet
- refrigerated storage room
- grading conveyor belt
Part by part
- 1
refrigerated storage room
- Watch for
- soft spots, hidden mould, odour shifts, moisture pockets
- AI action
- read temperature, humidity, headspace gases; trigger spoilage risk alerts; mark suspect bags for quarantine
- 2
grading conveyor belt
- Watch for
- surface defects, moisture stains, discoloration, trapped slurries
- AI action
- analyze imaging data; classify grade; flag suspect lots for removal and re-grade routing
- 3
handheld aroma detector unit
- Watch for
- off-odours, VOC spikes, terpene imbalances
- AI action
- signal sampling and generate VOC trend report; archive results to LIMS with timestamp
- 4
perimeter air sensor array
- Watch for
- mould spores near processing zones, elevated humidity
- AI action
- warn sanitation team; adjust airflow or zoning; log actions in LIMS
- 5
digital LIMS tablet
- Watch for
- missing lot codes, mismatched grading, incomplete records
- AI action
- update inventory; produce traceability report; alert QA when data gaps occur
- 6
gas sensor array
- Watch for
- gas concentration anomalies within storage
- AI action
- issue corrective action ticket and recalibrate sensors; store action history
Worked example
Catching rot drift before packing and shipment
Illustrative scenario with sample data.
A 1,200-tree oak-hosted truffle orchard, producing Tuber aestivum, age 12–15 years, prepares for first post-harvest run. AI watches storage rot risk at grading and packing.
Signal in
lotID=TRF2026-11, temp=2.0C, humidity=96%, VOCs=moderate, imageScore=0.66, detector=handheld aroma unit, weight=12.4kg per bag
System out
riskFlag=RotLikely; action=segregate batch TRF2026-11-04, initiate sanitation cycle, re-grade; update LIMS with lot history and chain-of-custody
Human sign-off: Post-harvest supervisor approves removal and triggers sanitation, re-grade, and packaging adjustments in the LIMS.
How to roll it out
Phase 1
Weeks 1 to 4
Baseline sensor network plan, data schemas, governance, and initial field test of grading and packaging integration.
Phase 2
Weeks 5 to 8
Full hardware install, camera calibration, and integration with LIMS for real-time alerts and traceability across lots.
Phase 3
Weeks 9 to 12
Operator training, SOP validation, dry runs for grading, removal, and packing with sign-off rituals.
Where people stay in charge
- Decisions to classify rot risk or remove a lot must be signed by the post-harvest supervisor and food safety authority.
- QA documentation and export sign-off must be performed by authorized personnel before shipment.
- Handlers must be certified for using aroma detectors and PPE with documented training records.
Questions owners and crews ask
What signals indicate a truffle is at risk of spoilage in storage?
Rot risk signals include rising humidity, temperature anomalies, VOC spikes, and deteriorating image quality; AI correlates these to generate a spoilage alert and action plan.
How does grading interact with AI spoilage detection?
AI models inspect surface features, mould indicators, and mass balance on the grading belt; flagged truffles are re-evaluated or removed, with records updated in LIMS.
What hardware is involved in AI spoilage detection?
Cameras on the grading belt, environmental sensors in storage, handheld aroma detectors, and a digital LIMS tablet for traceability and audit trails.
How is safety and compliance maintained for export-ready truffles?
AI reports are signed off by the post-harvest supervisor and food safety authority; lots are traceable from harvest to packing, with full lot history available.