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TOMRA Spectrim with LUCAi
Apple defect grading using whole-fruit deep-learning context.
What it does
Spectrim with LUCAi combines multi-wavelength imaging with deep learning for apple quality grading. Its models use whole-fruit context to help distinguish natural stem features from defects, with operator control over classifications and grading tolerances.
Who it is for
Apple packhouses selecting or upgrading a TOMRA optical grading line.
What stands out
- Multi-wavelength apple imaging
- LUCAi deep-learning defect classification
- Operator-controlled grade tolerances
Limitations to consider
- Internal-quality assessment uses separate Inspectra technology.
- Specific crop models and existing-line upgrade eligibility need confirmation.
Integrations & connections
Tasks it can help with
Sources & verification
This profile is based on vendor documentation, checked Oct 24, 2026. It is not a hands-on review or a performance benchmark. Unknown details are marked explicitly.
Current Spectrim and LUCAi application scopeApple-specific whole-fruit grading and operator workflowCurrent LUCAi models and grading systemsSuggest a correction