AI System for Opening Greenhouse Climate Variances for Review

The problem

In day-to-day operations, a localised climate issue may be hidden by routine averages. When temperature, humidity, ventilation, irrigation, outdoor weather and maintenance records are scattered across sensors, documents, field records or team systems, issues do not become visible in time.

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

A common exception is that maintenance, an open door or sensor calibration can cause a temporary variance. Without this context check, incomplete or incorrect automation could lead to an unsuitable climate adjustment that damages the crop, so no physical or operational action can be applied directly.

What we set out to improve

• Proportion of alerts the agronomist confirms as genuine operational variances
• Proportion of outputs corrected or rejected by the agronomist or greenhouse agricultural engineer
• Time between the source event and the inspection output

How the system works

1. 01 Define the review scope and the authorised agronomist or greenhouse agricultural engineer role
2. 02 Retrieve inputs: temperature, humidity, ventilation, irrigation, outdoor weather and maintenance records
3. 03 Check source identity, date, calibration or version, and data recency
4. 04 Check sensor recency and zone identity
5. 05 Compare values with the relevant period and outdoor weather context
6. 06 Check for an exception: maintenance, an open door or sensor calibration can cause a temporary variance
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 agronomist or greenhouse agricultural engineer approve, correct, defer or reject the output

Methods we used

• Zone-level threshold and time-series variance checks
• Source, format, timing and required-field checks for temperature, humidity, ventilation, irrigation, outdoor weather and maintenance records
• 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, greenhouse variance review list and human decision

Where people stay involved

Data and security

Access to temperature, humidity, ventilation, irrigation, outdoor weather and maintenance records 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 agronomist's or greenhouse agricultural engineer'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