Crop protection control with AI — a mislabeled drum is not an incident, it is a recall and a penalty.

The problem

Three realities that are almost never in the same place.

A correct drum of crop protection product is the intersection of three pieces of data that, in most plants, each live on their own island:

1. What the batch contains — the recipe for the active ingredient and the adjuvants. It usually sits in the ERP, in a vertical MES, or in a spreadsheet kept by the R&D manager and updated every season.
2. Which authorization covers the SKU in each destination country — the MAPA registration number in Spain, DGAV in Portugal, SENASA in Argentina, ANVISA in Brazil, the new EUDR registration for products with deforestation risk. Every country, its own H/P statements, its own pictogram, its own language. The label matrices live in PDFs and in the regulatory manager's head.
3. What happened on the line before — the production order and the CIP cleaning between campaigns. A trace of glyphosate in the next campaign's fungicide is cross-contamination — and a crop lost in the field.

The quality manager knows it, but cannot be in all three places at once. The operator has three screens and two checklists, and the changeover between campaigns is precisely the most vulnerable moment. The classic system works 99% of the time. That other 1% is what reaches the inspector — and every time it means a recall, a penalty and a distributor rethinking the next order.

How it fits the IRIS system

iLEAN does not add a fourth system — it seals the cracks between the three you already have.

The problem in crop protection control is not a lack of information: it is information living on islands that, at the critical moment (the SKU changeover, the campaign changeover, the EUDR registration update), does not reach the decision-maker in time. iLEAN acts as the putty that fills those gaps, without asking you to change your ERP, your MES or the filling line.

Edge sees the label and the filling operation in line. Connect reads the recipe, the LIMS and the registration matrix wherever they live. The agent cross-checks against the authorization in force and, if something does not add up, holds the drum before the pallet. The person signs — never the other way round.

The three iLEAN pieces applied to crop protection control:

• Edge — a terminal with machine vision (CNN) over the filling line: it reads the label printed on the drum (H/P statements, registration number, language) and cross-checks it against the pallet's destination. It also captures the active ingredient weighing, the fill level and the VOC reading in the booth. If something does not add up, it triggers a status light and a rejecter before close. It works with no network.
• Connect — captures the batch recipe whether it comes from the ERP, the vertical MES or the regulatory manager's spreadsheet. And it also captures what arrives from outside (a MAPA authorization update, a new CLP H/P statement, an email from the Argentinian distributor requesting a labeling change, an EFSA alert about an active ingredient) at second zero, with nobody forwarding anything.
• Agent — cross-checks the batch recipe, the active ingredient analysis, the CIP cleaning between campaigns, the printed label and the authorization in force for each destination country. If there is a deviation, it does not send an email at 10 p.m.: it holds the drum and alerts the quality manager through whatever channel they use. It also generates the per-batch conformity dossier, ready for inspection or for the distributor. The person validates and signs; the line does not restart on its own.

See the full IRIS architecture →

Before and after

Manual control vs. cross-checked control with iLEAN

Aspect	Manual control + checklist per campaign	With iLEAN Edge + Connect + Agent
GHS labeling by destination country	Label matrix in PDF, manual review	Automatic cross-check on every drum before close
Verifying the EFSA/MAPA registration number	The operator looks up the current list	Edge reads the label and cross-checks it against the registry in a second
CIP cleaning between campaigns	Paper checklist, signed by the shift lead	CIP traceability cross-checked with the production order
Detecting cross-contamination	Lab analysis after the changeover (hours)	Predictive alert if the cleaning has not been closed out
Dossier for a 1107/2009 inspection	Rebuilt by hand, weeks	Per-batch dossier, automatic, with a photo of the filling
Operation with no network	n/a	Edge keeps running on the panel's own light

Impact estimate

Impact estimate for your plant — to be validated with your numbers.

The block below is an estimate to be validated with the specific data of your plant. We put it forward so the committee has an order of magnitude; we refine it during the diagnostic.

• Mid-sized multi-country crop protection plant (herbicides, fungicides, insecticides), 2 drum/jerrican filling lines running multi-SKU and multi-registration, seasonal campaigns.
• Edge pilot on one line (camera over the label + active ingredient weighing sensor + VOC sensor in the booth + integration with ERP/MES and the registration matrix). First value expected in a few weeks.
• Indicative payback between 4 and 9 months, depending on the frequency of recalls over wrong labeling, 1107/2009 inspection non-conformities and distributor claims documented in recent years.
• The hard lever is a single recall avoided in one country: drums collected from the distributor, reverse logistics, destruction of the regulated active ingredient, brand damage and a review of the registration. One recall pays for the pilot. The expected reduction in in-line rejects is ≥ 30% over the current baseline.

And the regulatory manager's reasonable doubt

“What if the AI misreads the H/P statement and lets a mislabeled drum through?” — hallucination is a problem of free generation, not of anchored tasks. In tasks where the AI merely reads a label and compares it against a registration in force, the best models brought error below 1.5% ^[1]. And even so, what is critical is never decided alone: iLEAN holds the drum and the person signs. The three safety rings exist precisely for this.

^[1] OpenAI paper “Why Language Models Hallucinate”, 2025 — on the reliability of AI in anchored tasks.