AI System for Routing Field Images to Site Inspection

The problem

In day-to-day operations, areas requiring inspection across large plots may be identified late. When time- and plot-referenced field images, crop-cycle details, weather records and field notes are scattered across sensors, documents, field records or team systems, issues do not become visible in time.

This fragmented data delays preparation of the plot inspection map and forces teams to search for supporting evidence again by hand. Before making a decision, the agricultural engineer or agronomist must complete missing records and verify each conflict individually.

A common exception is that shadows, irrigation marks or the camera angle can create a similar appearance. Without this context check, incomplete or incorrect automation could flag a healthy area as problematic or miss a genuine issue, so no physical or operational action can be applied directly.

What we set out to improve

• Proportion of flags that the specialist confirms require a site inspection
• Proportion of outputs corrected or rejected by the agricultural engineer or agronomist
• Time between the source event and the inspection output

How the system works

1. 01 Define the review scope and the authorised agricultural engineer or agronomist role
2. 02 Retrieve inputs: time- and plot-referenced field images, crop-cycle details, weather records and field notes
3. 03 Check source identity, date, calibration or version, and data recency
4. 04 Match each image to its plot and date
5. 05 Compare visual differences with neighbouring areas and historical images
6. 06 Check for an exception: shadows, irrigation marks or the camera angle can create a similar appearance
7. 07 Route low-confidence or conflicting records to a separate specialist queue
8. 08 Write the source identity, rule, output and timestamp to the audit trail
9. 09 Have the agricultural engineer or agronomist approve, correct, defer or reject the output

Methods we used

• Preclassification of image differences without suggesting crop conditions or chemical applications
• Source, format, timing and required-field checks for time- and plot-referenced field images, crop-cycle details, weather records and field notes
• A missing-data warning instead of an estimate when inputs are incomplete or conflicting
• Separate evaluation of exceptions and the main rule
• Combined retention of the source, rule result, plot inspection map and human decision

Where people stay involved

Data and security

Access to time- and plot-referenced field images, crop-cycle details, weather records and field notes is limited to the minimum permissions required for the task. Commercial, location and employee information is masked where possible, and only the necessary subset of the raw dataset is passed to the model. Access, outputs and the agricultural engineer's or agronomist's decisions are logged; the system is not released to production until the organisation approves the retention period and data location.

Who this suits

Frequently asked questions