01 · MetricsWhat is happening now?
Indoor relative humidity68%
Updated 2 minutes ago- current value and unit;
- update time and data source;
- a quick chart for one metric;
- manual entry for users with permission.
Agricultural production data analysis
Farm metrics and analytics: from a current value to an informed decision
Gros.farm brings together manual measurements, sensor data, weather services, and calculated metrics. Compare actuals with tech-card targets, investigate deviations, and see which production events preceded a change.
- Start without sensors
- Multiple sources for one production metric
- History tied to a facility and growing cycle
Context for a point on the chartActual · target · eventReview data in the context of the growing stage
Manual measurements
Sensors and local systems
Weather services
Calculated metrics
Tech-card targets
Two ways to work with production data
Metrics show what is happening now. Analytics shows how it changed over time.
These are connected but distinct workspaces. Daily checks should not require a complex report, and investigating a deviation should not be limited to one current-value card.
02 · AnalyticsWhat changed, and when?
- several time series on one chart;
- preset and custom time ranges;
- minimum, maximum, and average;
- tech-card targets and events on the timeline.
Important: a metric represents the production meaning of data, not a sensor name.
More than sensors
Collect actuals using the sources available to your operation
Begin with manual observations and weather data. Add sensors and calculated values later without changing the production meaning or history of the metric.
Manual entry
Record a measurement at the facility or assign the observation to a team member with the appropriate role.
Sensors
Map an available sensor or measurement point to a production metric and the relevant zone.
Weather data
Use available outdoor temperature, humidity, precipitation, wind, and light data without changing the metric workflow.
Calculated values
Derive metrics such as VPD, DPD, dew point, and radiation sum when the required source data is available.
Plan and actuals on one chart
A tech card turns a data line into a production-control tool
A value alone does not tell you whether conditions are appropriate. Gros.farm places it beside the target range for the current stage, with the crop, cycle, and day or night context.
- see both the size and duration of a deviation;
- the target context changes when the growing stage changes;
- actuals stay in history for review and the next crop-plan revision.
Target rangeActual value
Deep analysis
Put the time series needed for a hypothesis on one chart
Choose a facility or production zone, crop, batch, or production flow and set a period. Then enable the relevant metrics and review them on the same timeline.
Choose the context
Select a facility or zone, crop, batch, or production flow.
Set the period
Use 12, 24, or 72 hours, 7 days, or a custom time range.
Enable relevant series
Keep only the metrics needed for the hypothesis being investigated.
Inspect the detail
Compare lines, zoom into a period, and review minimum, maximum, and average.
Production context for the timeline
Events help explain what changed in the production process
A chart can show the moment a cycle moved to a new tech-card stage. The data change can then be reviewed alongside the change in targets and operating mode.
April 28 · 14:51Moved to the “Forcing” stage
Review how growing conditions and actual facility behavior changed after the event.
Without an event, the chart shows a line. With an event, it gains production context.
Context for the AI workspace
Data history becomes structured experience that models can work with
A connected model can receive time series together with target ranges, stages, and events. This supports pattern exploration and helps formulate hypotheses for review.
AI can help investigate data, but it does not establish causation automatically. Decisions remain with the grower, production technologist, or other responsible specialist.
Growing-cycle contextData + production technology
- time series;
- target ranges;
- growing stages;
- events and actions;
- history from previous periods.
One fact base for different roles
The team discusses shared data, not competing versions of events
A manager, grower, engineer, and external expert can open the same period and see the same values, targets, and events without retelling or scattered screenshots.
Grower and production technologist
Compare actual conditions with stage targets and investigate production hypotheses.
Engineer
Review source history and how the local control layer maintained the operating mode.
Manager
See trends and deviations across facilities without manually assembling reports.
External expert
Work from the same context as the team: data, stages, targets, and events.
Key questions
Farm metrics and analytics FAQ
Can we track production metrics without sensors?
Yes. Start with manual entry and available weather data. Later, connect sensors and calculated sources to the relevant metrics without losing the production meaning or accumulated history.
How is the Metrics screen different from Analytics?
Metrics is for current values, source, update time, manual entry, and a quick chart for one parameter. Analytics is for deeper comparison of several time series, periods, statistics, tech-card targets, and production events.
Can one metric use multiple sources?
Yes. A production metric can have several sources or measurement points. Map each source to the relevant metric and production zone so its meaning remains clear.
How are actual metrics compared with tech-card targets?
The target range comes from the current stage of the tech card and can distinguish day and night requirements. The chart places the actual value beside the active target so both the size and duration of a deviation are easier to assess.
Which periods can be analyzed?
Choose 12, 24, or 72 hours, 7 days, or a custom range. Chart zoom supports closer review of a specific part of the history.
Can AI identify the cause of a deviation automatically?
A model can compare time series, targets, stages, and events and help formulate a hypothesis. Correlation alone does not establish causation, so the responsible specialist validates the interpretation using crop biology and production context.
From values to production decisions
Start collecting production data and connecting it with crop technology
Begin with manual metrics and weather data, then add sensors, calculated values, and deeper analytics as the operation needs them.