Why it matters
Without AI grading, inconsistent size and defects cause rejected lots, mispriced shipments, and uncertain inventory. Human grading varies with fatigue, lighting, and subjective perception, hindering documentation and traceability.
From design to safety
Design
AI grading concept design
Define target grade classes, imaging standards, and data schema for weight, shape, firmness, defects, and photos to support repeatable decisions. Specify camera angles, lighting, gloss control, and color calibration for consistent image capture. Build data dictionaries for lot identifiers and provenance.
- grading classes
- weight
- photos
Build
Data pipelines and sensors setup
Install calibrated scales with 0.1 g resolution, color-calibrated high-dynamic-range cameras, 3D surface scanners, and embedded moisture sensors. Create data ingestion pipelines, metadata schemas, and validation rules for every harvest event.
- grading classes
- weight
- photos
Operation
Live grading workflow
Process incoming truffle lots with AI model inference on size, shape metrics, firmness, defects, and photo quality; assign grade, batch ID, storage destination, and export eligibility with a complete audit trail.
- grading classes
- weight
- shape
- photos
Maintenance
Model maintenance and calibration
Periodically retrain with new lots, adjust thresholds for size bands, textures, and defects, and verify grade consistency across operators and batches. Recalibrate image sensors after seasonal changes and update validation files.
- grading classes
- shape
- firmness
Safety
Quality control governance
Ensure trained personnel audit AI outputs, maintain traceability records, enforce food safety and export compliance through documented approvals, and manage non-conforming lots with quarantine workflows and regulator notifications.
- defects
- photos
- grading classes
Part by part
- 1
Scalable grading software core
- Watch for
- Drift in grade boundaries or misclassification of rare defects to catch.
- AI action
- Detects misclassified lots and flags for human review before packaging.
- 2
High-resolution imaging station
- Watch for
- Image artifacts or lighting variance affecting shape metrics.
- AI action
- Adjusts exposure and angle prompts; flags poor images for retake.
- 3
Precision scales with tare and moisture compensation
- Watch for
- Weight anomalies due to moisture pockets or scale drift.
- AI action
- Normalizes weight into standard size bands and alerts for reweighing.
- 4
Texture and surface scanner
- Watch for
- Surface imperfections that indicate defects or contamination.
- AI action
- Scores surface integrity and adds notes to grading; triggers inspection.
- 5
Defect database and quarantine log
- Watch for
- Mislabeling, foreign matter, or mold indicators.
- AI action
- Records defect types, timestamps, lot IDs; routes to quarantine if needed.
- 6
Automated photo capture and archive
- Watch for
- Missing photos for validation or inconsistent framing.
- AI action
- Stores standardized images per lot and links to grade record.
Worked example
Automated grade run at a 5-year-old oak orchard
Illustrative scenario with sample data.
A mid-season batch of Tuber melanosporum is graded for export and local markets using AI-assisted size, shape, and quality scoring.
Signal in
lot_id: L-483, harvest_date: 2026-11-02, host: Quercus robur, quantity: 68 kg, weight: [15, 18, 20, 12, 3] kg, shape_metric: {elongation: 0.92, skew: 0.15}, firmness_score: 7.2, defects: 1, photo_count: 6, moisture: 0.12
System out
grade: A2, batch_label: B-L-483-A2, storage: cold_room_01, notes: minimal surface residues, send to export packer.
Human sign-off: Quality supervisor reviews AI outputs and approves grade with batch label and storage assignment.
How to roll it out
Phase 1
Weeks 1 to 4
Baseline data collection, imaging workflow calibration, and initial rule setting for size bands.
Phase 2
Weeks 5 to 8
Pilot AI grading on a subset of lots, compare with human graders, adjust thresholds and image prompts.
Phase 3
Weeks 9 to 12
Full production rollout with automated recordkeeping, regular audits, and integration with packing line.
Where people stay in charge
- Decisions on grade classification and line assignment must stay with a trained quality manager due to export and food safety implications.
- All grading outputs must be auditable with a verifiable chain of custody and signed by QA.
- Audits of AI outputs must be signed off by a certified QA lead and food safety officer before shipment.
Questions owners and crews ask
How does AI grading handle size and weight measurements for truffles during busy harvests?
AI grading uses calibrated scales and standardized imaging to compute size bands; operators verify outputs, and the system flags discrepancies for manual check.
Can AI detect surface defects that affect market value?
Yes, the imaging and texture scanner analyze surface roughness, blemishes, and contaminant indicators, routing suspect lots to human inspection.
What records are produced for export certification?
Grade, lot ID, storage location, defect notes, moisture reading, and images are archived with timestamps for traceability and compliance.
Who approves grading decisions for regulatory compliance?
A trained QA manager and food safety officer sign off before any shipment, with periodic audits by the regulatory authority.