Agrifood traceability with AI: from the field batch to the customer's pallet.

What two-way traceability is, in one sentence

Two-way traceability means that from a pallet standing on an end customer's loading bay you can walk the path backwards — box, batch, processing line, incoming raw material, plot of origin, member grower, agronomy applied — and forwards as well: if a plot applies a treatment, you know immediately which pallets at which customers could be affected.

It is not optional. In modern agrifood it is the basis of any conversation with the large retail chains, with IFS/BRC audits and with the authorities.

What the regulation requires (as at 2026)

Three relevant Spanish and European rules:

• Regulation (EC) 178/2002 — The legal basis for food traceability in the EU. It defines the mandatory "one step back, one step forward" for every operator.
• Royal Decree 1051/2022 — Establishes the mandatory digital notebook on Spanish farms. It is the piece connecting the agronomic side to the industrial one.
• FSMA 204 (USA) — If you export to the United States, it makes a digital record of critical tracking events mandatory for certain products.

The lowest common denominator: every batch in and every batch out, with real shelf life recorded. What varies between operators is how much detail above that minimum you add for your own control.

The technologies that connect

A modern traceability architecture integrates five pieces:

• Digital field notebook — Point zero. Without it, farm batch data is an intermittent spreadsheet.
• Plant ERP / warehouse management — Where the processing, waste and stock information lives.
• Weighing and automatic labelling — Generating GS1-128 or GS1 DataMatrix codes on the line, without friction.
• Cold chain sensors — Temperature and humidity by chamber, container or lorry.
• The AI layer — The new part. It cross-references everything above and spots inconsistencies before a person does.

All five are APIs today. The problem is no longer technical, it is one of integration and data ownership: which is the single source of truth when three systems are recording the same event.

What AI adds

Three concrete things, no hot air:

• Anomaly detection — A batch with a parameter outside the pattern flags immediately, before moving to the next process. This reduces complaints from the end customer.
• Shelf life prediction — Models cross-referencing treatment history, intake quality and cold chain data to estimate the real best-before date rather than the conservative labelled one. It allows stock rotation to be optimised.
• Instant reverse lookup — An alert on a field batch returns, in under a minute, every dispatched pallet sharing that ancestry. It used to be a morning in a spreadsheet.

What it costs

Honestly: it depends. For a mid-sized processing business (€15-50M turnover), a full traceability implementation with AI runs between €60,000 and €180,000 in the first year, depending on how many integrations with legacy systems it requires.

The return comes through two routes: the direct one (faster audits, fewer complaints, better negotiation with large accounts demanding demonstrable traceability) and the indirect one (the ability to optimise waste and rotation because for the first time you hold the real figures).

Conclusion

Traceability is no longer a compliance project. It is the infrastructure everything else is built on — quality, efficiency, sustainability, commercialisation. If you are still wrestling with spreadsheets between field and plant, the real cost is probably not that of putting traceability in place. It is the opportunity cost of going without it.