Why it matters
Without it, inconsistent gleba grading can lead to rejected batches, uneven pricing, and storage issues that threaten product integrity and export compliance.
From design to safety
Design
AI guidance for grading criteria
The system defines data fields, imaging standards, grading rules, and maturity benchmarks from gleba colour scales, marbling indices, and brulé integrity, to support consistent post-harvest grading and clear acceptance criteria across lots. It prescribes required data capture points: orchard lot IDs, brulé health notes, host-tree IDs, and seasonal maturity allowances, plus mapping to packing standards and export preparation.
- gleba imaging station
- color calibration panel
- sample cut templates
Build
Data collection setup and calibration
It integrates high-res cameras, hyperspectral sensors, reflectance modules, and test-cut logging to establish baseline gleba profiles and maturity thresholds for each batch, with secure data vaulting. Installation includes ceiling-mounted lighting rigs, anti-vibration mounts, enclosure-rated data racks, and a centralized API gateway for on-farm network segmentation.
- ultra-high-resolution camera
- spectral sensor array
- test-cut kit
- calibration phantom
Operation
Real-time maturity and gleba evaluation
AI analyzes gleba colour, marbling, texture, and connective tissue patterns from images and test cuts, assigning maturity scores and grading categories for immediate QC decisions and batch routing. It cross-references storage temperatures, gel-pack history, and brulé health flags to anticipate texture changes during cooling and to support rapid re-pack decisions.
- gleba imaging station
- test-cut templates
- grading workstation
Maintenance
Model drift monitoring and re-baselining
Regular updates from new harvest cycles, storage temperature shifts, and brulé health variations keep gleba scoring aligned with physical changes and seasonal variation across orchards. It schedules quarterly re-training, logs data version history, and enforces access control for model updates.
- data logger
- quality control database
- calibration phantom
Safety
Traceability and audit readiness
All assessments are logged with lot IDs, harvest date, storage location, responsible signer, and chain-of-custody details to ensure export and food safety compliance. It enforces role-based approvals, links to certification bodies, and maintains tamper-evident records for audits.
- henry lot trace system
- digital audit trail
- certified storage log
Part by part
- 1
gleba imaging station
- Watch for
- Unusual colour shifts or marbling patterns that deviate from defined gleba baselines.
- AI action
- Flagged for confirmatory human inspection and grading adjustment.
- 2
color calibration panel
- Watch for
- Drift in color readings across sessions or lighting inconsistencies.
- AI action
- Trigger re-calibration and data normalization.
- 3
sample cut templates
- Watch for
- Inconsistent cut depths or missing reference points in test cuts.
- AI action
- Normalize data and prompt quality team for re-cut sampling.
- 4
ultra-high-resolution camera
- Watch for
- Resolution drop or blur affecting colour and marbling detail.
- AI action
- Prompt technician to realign lens or replace optics.
- 5
spectral sensor array
- Watch for
- Spectral anomalies indicating non-target compounds in gleba or contamination.
- AI action
- Trigger contamination check workflow and sample reanalysis.
- 6
grading workstation
- Watch for
- Missing or inconsistent grading categories across lots.
- AI action
- Suggest corrective grading routes and generate updated batch labels.
Worked example
Gleba Insight for a 5,000-tree orchard
Illustrative scenario with sample data.
During post-harvest, inconsistent gleba readouts were causing grade disputes between buyers and distributors.
Signal in
lotID: L-0472, harvestDate: 2026-10-12, treeTypes: T. melanosporum, storageTemp: 2C, imaging: yes, testCuts: 3 per pallet
System out
QA report generated: maturityScore=7.2/10, grade=B, anomalies: colorShift-1.1, recommended re-cut sampling for pallet P-1123, notification sent to QC lead.
Human sign-off: QC lead reviews the report, approves re-cut sampling, and signs off on batch G-1123.
How to roll it out
Phase 1
Weeks 1 to 4: Baseline
Install imaging and sensing hardware; establish initial gleba baselines and grading rules for key harvests and brulé health.
Phase 2
Weeks 5 to 10: Validation
Run parallel human QC checks; tune AI thresholds against expert grader scores and adjust workflows for seasonal changes.
Phase 3
11 to 16 weeks: Operational
Full post-harvest integration; automated scoring, labeling, and archive of lot-level records with audit trails.
Where people stay in charge
- All grading decisions affecting price or export status must be signed by a licensed quality control supervisor and stored in the official registry.
- Food safety and traceability decisions require approval by the authorized storage manager and records keeper with digital signature.
- Any change to imaging or maturity thresholds must be reviewed by the agronomist and archived with version history and rationale.
Questions owners and crews ask
How should gleba colour be standardized across farms?
Standardize with a fixed color calibration panel, controlled lighting, and a reference set of colour chips; document baselines in the quality system.
Can AI detection catch mixed-truffle lots?
Yes, by correlating imaging data with lot IDs, storage metadata, and test-cut results to identify inconsistent maturity signatures.
What records are needed for export compliance?
Lot ID, harvest date, storage conditions, grading results, and sign-off by the licensed QC supervisor must be retained.
How is test-cut data integrated into grading?
Test-cut results feed into the maturity score and grade category, linked to lot-level records and imaging evidence.