Use cases
Agricultural problems, addressed with hardware already in the field.
Each of these is a decision a farm is taking today with less information than it would like. Terra raises the resolution of what is known. It does not take the decision.
01 · Use case
Crop Health
Crop stress is visible before it is severe, but only to someone standing in front of it. On a large block, the interval between one walk and the next is the interval in which a problem grows.
How Terra approaches it
Terra Vision analyses imagery from existing cameras and drone flights for colour, texture and canopy change, and compares each zone against its own earlier record. Terra Intelligence reads those changes against soil moisture and weather, so a change is interpreted rather than merely noticed.
What comes out
- Per-zone health indicators
- Visual change measured over time
- Suspected stress raised with evidence
- Zones ranked for inspection
CAM-04 · Zone D · 12:40
3 detections
Suspected findings · confirmation required
02 · Use case
Pest Intelligence
A pest count on its own says almost nothing. What matters is whether pressure is rising, where it is concentrated, and whether it is spreading toward blocks that are still clean.
How Terra approaches it
Terra Pest counts detections from camera imagery and compatible monitoring hardware, tracks them per zone over time, and correlates rising counts in neighbouring zones. Environmental conditions that favour development are read alongside the counts.
What comes out
- Counts by zone and period
- Hotspots and direction of spread
- Outbreak risk raised early
- A prioritised scouting list
Pest pressure by zone
6 weeks · detection level
| Zone | W1 | W2 | W3 | W4 | W5 | W6 |
|---|---|---|---|---|---|---|
| Zone A | Zone A, week 1: none. | Zone A, week 2: none. | Zone A, week 3: trace. | Zone A, week 4: none. | Zone A, week 5: none. | Zone A, week 6: trace. |
| Zone B | Zone B, week 1: trace. | Zone B, week 2: none. | Zone B, week 3: none. | Zone B, week 4: trace. | Zone B, week 5: trace. | Zone B, week 6: none. |
| Zone C | Zone C, week 1: none. | Zone C, week 2: trace. | Zone C, week 3: none. | Zone C, week 4: none. | Zone C, week 5: trace. | Zone C, week 6: trace. |
| Zone D | Zone D, week 1: trace. | Zone D, week 2: trace. | Zone D, week 3: low. | Zone D, week 4: low. | Zone D, week 5: rising. | Zone D, week 6: high. |
| Zone E | Zone E, week 1: none. | Zone E, week 2: trace. | Zone E, week 3: trace. | Zone E, week 4: low. | Zone E, week 5: low. | Zone E, week 6: rising. |
03 · Use case
Disease Risk
Leaf symptoms and favourable weather are each ambiguous alone. Together they are the reason one block needs looking at today and another does not.
How Terra approaches it
Terra Vision surfaces suspected symptoms from imagery; Terra Climate supplies humidity duration, temperature and rainfall; Terra Intelligence combines them into a scored risk that names both contributions.
What comes out
- Suspected symptoms with confidence
- Environmental risk in context
- Elevated-risk zones identified early
- Findings routed for agronomic confirmation
Site conditions
4 observed · 3 forecast
Daily range, °C
31
33
34
35
36
37
35
Hours above humidity threshold
4
5
7
9
11
12
10
Mon
Tue
Wed
Thu
Fri
Sat
Sun
observedforecast
04 · Use case
Soil Monitoring
A field is rarely uniform. Averaged across a block, a dry corner and a saturated one produce a number that describes neither.
How Terra approaches it
Terra Soil normalises readings from connected probes, holds them per zone and depth, and compares zones against each other and against their own history. Anomalies are separated into agronomic change and instrument fault.
What comes out
- Moisture, temperature and EC by zone
- Zone-to-zone comparison
- Trends read against the zone's history
- Instrument faults caught, not passed on
Moisture at depth
% volumetric
| Depth | Zone A | Zone B |
|---|---|---|
| 10 cm | 58 | 34 |
| 30 cm | 64 | 41 |
| 60 cm | 69 | 58 |
05 · Use case
Irrigation Intelligence
Irrigating uniformly wastes water in the zones that do not need it and under-serves the ones that do, and the difference is usually not visible from the pump house.
How Terra approaches it
Soil moisture behaviour, canopy stress signals and environmental demand are read together per zone. Terra Optimize ranks the zones by severity and by how much of the evidence agrees, and shows the readings behind each recommendation.
What comes out
- Water-stress probability by zone
- Ranked irrigation priorities
- Evidence attached to each recommendation
- Advisory only: the farm decides
Recommended next actions
Irrigation review
Zone B · priority High
Moisture below the zone's own baseline for four days, with canopy stress rising in imagery and three days above the usual maximum.
Scouting priority
Zone D · priority High
Detection counts up across three capture cycles, with the neighbouring zone showing the same direction of travel.
Instrument check
Zone E · priority Medium
Moisture channel static for 41 hours while neighbouring probes responded to the same rainfall. Treated as a data-quality problem, not a soil condition.
06 · Use case
Farm Monitoring
Coverage is the constraint on any large operation. Adding people scales linearly with area, and adding a proprietary hardware estate is a capital project before it is an improvement.
How Terra approaches it
Terra runs on the cameras, sensors, gateways and weather sources a farm already owns, normalises what they produce, and reports by exception: the platform's job is to be quiet until something warrants attention.
What comes out
- Continuous coverage without new hardware
- Exception-based alerting
- One record across the whole operation
- Instrument health tracked with the data
Soil moisture · Zone B · 30 cm
14 days · % volumetric
07 · Use case
Yield Intelligence
Yield expectations formed at the start of a season and revised only at harvest leave a long stretch in which planning is guesswork.
How Terra approaches it
Counted fruit and stand data from Terra Vision, growth stage from Terra Twin, and the season's conditions from Terra Climate are held together, so a forecast can be revised against what the farm has actually done and what this season is doing.
What comes out
- Counted observations by zone
- Growth tracked against season history
- Condition-adjusted expectations
- Forecast inputs, stated as estimates
Model structure
Example Farm
2 fields · 5 zones · 56.7 ha
Field North
2 zones · tomato
Zone A
12.4 ha · 3 cameras · 6 sensors · healthy
Zone B
15.1 ha · 2 cameras · 5 sensors · water stress
Field South
3 zones · pepper, cucumber
Zone C
9.7 ha · 2 cameras · 4 sensors · healthy
Zone D
11.2 ha · 4 cameras · 4 sensors · pest risk
Zone E
8.3 ha · 2 cameras · 3 sensors · monitoring
08 · Use case
Precision Agriculture
Most agricultural decisions are still taken at the resolution of the whole field, because that is the resolution the available information supports.
How Terra approaches it
Terra raises the resolution of what is known: readings and detections resolved to zones, evidence retained per zone, and risk scored per zone. Where camera coverage is close enough, individual plants can be counted and tracked.
What comes out
- Zone-level state and risk
- Plant-level observation where imagery allows
- Decisions taken at the right resolution
- A record that improves with coverage
Signals
- Low soil moisture
- High temperature
- Canopy stress in imagery
Elevated water-stress probability
Raised as a probability with the contributing readings attached, and routed to irrigation priorities.
Signals
- High humidity duration
- Leaf symptoms detected
- Favourable temperature range
Elevated disease risk
Reported as a suspected condition requiring agronomic confirmation, never as a diagnosis.
Signals
- Rising pest counts
- Adjacent zones affected
- Conditions favour development
Possible infestation spread
Direction and pace are estimated from the zones involved, and inspection is prioritised accordingly.
Next step
Request a Farm Assessment
Every agricultural operation has a different data footprint. Terra deployments are scoped according to the farm, existing hardware and required intelligence capabilities.
The assessment establishes what your existing hardware can support before anything is committed.