Agricultural edge intelligence

ForgeAware-AG makes every local AI decision observable.

A purpose-built engineering observability application running on the ForgeEdge AI Core. It brings agricultural telemetry, system health, historical measurements and the evidence behind AI decisions into one local, read-only operational view.

Local-first operationDecision traceabilityAgricultural telemetryRead-only API
From data to evidence

See what happened, why it happened and what followed.

Agricultural automation produces more than sensor values. It produces decisions: a zone is flagged, a rule or model interprets the evidence, a command is issued and the field responds. ForgeAware-AG links those stages into a single trace that engineers and agronomists can inspect.

The application is designed as an observability layer. It reads telemetry and decision records through a controlled API without becoming an uncontrolled path into the automation system.

  • Live and historical sensor measurements with timestamps
  • Evidence, assessment, decision and field-result correlation
  • Connectivity, latency, service and storage health indicators
  • Daily, weekly, monthly and annual operational summaries
ALVEXIS24-hectare strawberry greenhouse● Edge online
OverviewLive monitoringHistory & trendsAI observationsDecision traceSystem health
Greenhouse climate31.8 °CHigh
VPD1.42 kPaNear limit
Root-zone moisture63%Normal
Zone statusUpdated 14:32
Zone 124.0 °C · 68%
Zone 223.4 °C · 66%
Zone 734.4 °C · 42%
Zone 824.1 °C · 65%
1 · EvidenceHeat stress + moisture loss
2 · AssessmentRapid root-zone drying
3 · DecisionStart local irrigation
4 · ResultMoisture restored
Core capabilities

Local visibility for the complete agricultural decision chain.

ForgeAware-AG combines operational monitoring with the engineering context needed to validate and improve field automation.

1

Live and historical data

Inspect sensor values, event records and timestamps across selectable time ranges, with continuous live views when required.

2

Decision traceability

Relate each decision to source sensors, processed values, model or rule context, confidence, command and field result.

3

System health

Monitor connectivity, data delay, last successful reception, service state, storage use and component health together.

4

Time-based summaries

Compare daily, weekly, monthly and annual trends, threshold events, decision distribution and operating time.

5

Evidence-led review

Answer not only what happened, but which data supported the decision, when it was made and how the system responded.

6

Local continuity

Keep essential data, decision records and application services on site so cloud connectivity is not required for automation continuity.

Application scenarios

Built around real agricultural operating zones.

  • Greenhouse climate and vapor-pressure monitoring
  • Root-zone moisture and irrigation decision review
  • Multi-zone anomaly and threshold-event analysis
  • Crop-environment trend comparison across seasons
  • Remote farm or protected-cultivation system health
Deployment boundary

Observability without cloud dependence.

ForgeAware-AG runs on the ForgeEdge AI Core beside the agricultural automation workload. The core monitoring path is local and can continue without an internet connection.

The standard application exposes and consumes controlled, read-only engineering data. Any actuation or control integration is treated as a separately engineered and validated system function.

See the rugged ForgeEdge platform →

Define a ForgeAware-AG pilot.

Share the crop, operating zones, available sensors, automation interfaces and decisions that must be made traceable. We will map the ForgeEdge configuration, local software integration and pilot acceptance evidence.