Produce Grading & Sorting AI

Add a real-time grading layer to every line with vision AI for produce grading and sorting. Built for the packhouses where no two pieces are alike and the line moves ten a second, this season's defect didn't exist last season, and the bruise that was invisible at pack is obvious by the time the pallet reaches retail. Whether you're receiving from the field, grading and sorting, or packing for a retail spec, Roboflow grades every piece consistently, with per-lot records behind every packout.

Grading and Defects:

• Catch bruises, cuts, rot, mold, scab, russeting, and sunburn on every piece, at line speed
• Grade color and ripeness stages consistently, so the pack that ships matches the spec that sold
• Classify size and shape against pack classes, and route culls out before they reach a premium carton

Sorting, Packing, and Receiving:

• Sort to lanes and pack specs by grade, and verify counts and patterns in trays, clamshells, and cartons
• Flag foreign material, stems, and leaves on the line before they reach a customer
• Grade at receiving with imagery attached to every lot, so pay-on-grade conversations start from the same photo

Seasons, Growers, and Systems Integration:

• Trend defect rates by grower, block, and harvest day, so a block's scab problem surfaces at receiving before it surfaces in packouts
• Retrain for this season's varieties and defects in days, without waiting on an equipment vendor's schedule
• Integrate with sorters, packline PLCs, and packhouse systems, with per-lot grade records that support USDA and GlobalG.A.P. documentation

Bring intelligence to every grade line today. Stop grading misses from becoming rejected loads, chargebacks, or the packout argument nobody can settle.

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More About Produce Grading and Sorting

What is produce grading and sorting with Vision AI?

Produce grading and sorting with vision AI uses computer vision models to grade fresh produce through the packhouse: bruises, cuts, rot, scab, russeting, and sunburn, color and ripeness staging, size and shape classification, foreign material flags, and count and pattern verification at pack. Models trained on your actual crops and varieties grade every piece at line speed, with per-lot records that support USDA grade standards, PTI traceability, and GlobalG.A.P. documentation.

Can Vision AI grade produce when no two pieces are alike?

Natural variation is exactly the case rule-based systems lose: every apple is a different shape, a stem cavity's shadow mimics a bruise, russeting on one variety is a defect and on another is the variety, and this season's weather wrote a blemish pattern last season never saw. Deep-learning models trained on your actual crops, varieties, and lighting learn the difference between variation and defect, hold one standard from the first bin of the season to the last, and route borderline pieces to a grader instead of guessing, which is how the pack stays consistent when the fruit isn't.

Does this replace our optical sorting machines?

No, not where you run them. High-speed singulated optical sorters keep doing what they were bought for on your biggest lines, and NIR and hyperspectral systems keep owning internal quality like brix and internal browning. Vision AI adds judgment where those machines don't reach: the receiving dock, the grading table, small and seasonal lines, and pack verification after the sorter, and it retrains for a new variety or this season's defect in days instead of waiting on a vendor's schedule. Sorter data and vision grades land in the same per-lot record.

Can it integrate with our sorters, packline PLCs, and packhouse systems?

Yes. Roboflow Inference exposes a standard API and supports common industrial protocols, so grading events flow into your existing systems: sorter and packline PLCs from Allen-Bradley and Siemens, packhouse and ERP platforms like SAP and Oracle, and SCADA and HMI platforms like Ignition and AVEVA, through REST, MQTT, OPC UA, and direct database writes. PLC-level integration diverts a cull or holds a lane the moment a grade fails, and every event carries grower lot, line, grade, imagery, and disposition, with a full audit trail behind every packout.

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