AI System for Monitoring Agricultural Input Stock and Expiry Records

The problem

In day-to-day operations, lot risks can be overlooked in high-volume stock. When product, lot, date, quantity, warehouse location and status records are scattered across sensors, documents, field records or team files, issues may not become visible in time.

This fragmented data delays preparation of the input stock review list and forces teams to search manually for supporting evidence again. The agricultural engineer and warehouse supervisor must complete missing records and verify each conflict before making a decision.

A common exception is a quarantine, return or special-storage status. Incomplete or incorrect automation that fails to check this context could lead to the use of an unsuitable input or the disposal of valid stock, so no physical or operational action can be carried out directly.

What we set out to improve

• Number of lot alerts verified by the specialist
• Proportion of outputs corrected or rejected by the agricultural engineer and warehouse supervisor
• Time between the source event and the review output

How the system works

1. 01 Define the review scope and the roles of the authorised agricultural engineer and warehouse supervisor
2. 02 Retrieve the inputs: product, lot, date, quantity, warehouse location and status records
3. 03 Check source identity, date, calibration or version, and data recency
4. 04 Sort lots by date and location
5. 05 Check quarantine status and open requests
6. 06 Check for exceptions: a quarantine, return or special-storage status may apply
7. 07 Place low-confidence or conflicting records in a separate specialist queue
8. 08 Write the source identity, rule, output and timestamp to the audit trail
9. 09 Have the agricultural engineer and warehouse supervisor approve, correct, defer or reject the output

Methods we used

• Lot-based date and status rules
• Source, format, timing and required-field checks for product, lot, date, quantity, warehouse location and status records
• A missing-data warning instead of an estimate when inputs are incomplete or conflicting
• A separate exception check outside the main rule
• Combined retention of the source, rule result, input stock review list and human decision

Where people stay involved

Data and security

Access to product, lot, date, quantity, warehouse location and status records is restricted to the minimum permissions required for the task. Commercial, location and employee information is masked where possible, and only the necessary portion of the dataset is sent to the model. Access, outputs and the decisions of the agricultural engineer and warehouse supervisor are logged; production use does not begin until the organisation has approved the retention period and data location.

Who this suits

Frequently asked questions