AI quality control and traceability for fresh produce
Quality tools grade the fruit. Traceability tools follow it. Pak-Bot does both from one database, so a failed inspection is linked to every pallet, invoice and customer it touched.
Questions that used to need a report export
- Which customers got the fruit that failed QC this morning?
- Show me the QC photos for the pallets on truck 4
- Failed inspections by supplier this month
- Customer complaints traced back to field or block
- Mock recall for batch 158
Traceability is captured at delivery, in each batch and at dispatch, so Pak-Bot can trace up and down the supply chain by batch, crop code, pallet, purchase order, invoice or customer order reference.
How AI quality inspection works
Choose the commodity
The inspector picks asparagus, baby spinach, apples or any other configured crop on a phone.
Take photos
For asparagus, both sides of the stem, to check circumference, tip integrity, rust and purple spot.
AI fills in the results
A computer vision model trained on your own criteria marks each one pass or fail.
A person approves
The inspector reviews and can override any result or choose a failure response. Failures alert buyers and growers with photo evidence.
Case study: seven packhouses, one standard
An anonymised grower, packer and distributor of about 36 short-shelf-life crops started at one packhouse in a pilot, then rolled out to all seven sites by month 2.
| Phase | Time per sample | AI accuracy | Status |
|---|---|---|---|
| Month 0 (pilot) | 14.5 min | 81.0% | One site, training and calibration |
| Month 1 | 8.7 min (−40%) | 88.5% | Expanded to 3 sites |
| Month 2 | 6.5 min (−55%) | 93.5% | All 7 packhouses; above human benchmark |
| Month 4 | 4.35 min (−70%) | 98.2% | Significant labour savings |
| Month 6 | 4.35 min (−70%) | 99.8% | Manual corrections under 0.1% |
"By month two, the AI was demonstrably more accurate than our human inspectors."
Shorter intake testing keeps rocket, watercress and herbs out of warm receiving bays, which protects the cool chain and shelf life. With inspection data in Pak-Bot and the quality dashboard, the team now relates harvest temperature to quality and gives growers more useful feedback.
Trained on your own defects
Producepak digitises your inspection criteria, your team captures inspections for about five months, then a model is built from your own data on Microsoft Azure. Management can tune results and rebuild the model whenever they choose.
Inspection types include pass/fail, percent of sample, scoring and value ranges such as sugar content. Producepak quality control
See Pak-Bot answer questions about your own produce
A Producepak consultant sets Pak-Bot up on your inventory, orders, quality and shipping data. Implementation typically takes one to three weeks, depending on how complex your business is.