CropYield

What is CropYield?

Field decisions get made on a mix of observation, habit and whatever the forecast said. CropYield works from what the field is actually showing — imagery, conditions, history — and produces diagnoses and irrigation recommendations with the evidence attached and the confidence bounded by it. A recommendation you can interrogate is one an agronomist can overrule intelligently, which is the only way software earns a place in that decision.

Problems it solves

• Diagnose crop problems from field imagery
• Plan irrigation against actual conditions rather than a schedule
• See the evidence behind a recommendation
• Track how a field has changed across a season
• Prioritise where to walk the field next

Key capabilities

Change Detection

Compare observations over time and flag what changed, where and by how much.

Evidence Trail

Every figure carries the record it came from, so a reader can check the answer instead of trusting it.

Confidence Ceilings

Confidence is capped by the strength of the underlying basis, so a weak source cannot produce a strong-sounding claim.

Forecasting

Project a pipeline, a harvest or a load curve, with the drivers behind the number visible.

Risk Scoring

Score a change, a lot or a habitat on a published rubric you can read component by component.

How it is built

Image diagnosis

Assess crop imagery and report what was observed, with the frame.

Condition-led irrigation

Recommendations follow measured conditions rather than a fixed calendar.

Bounded confidence

A diagnosis from thin evidence says so.

How it works

How the work is structured, and what each step's output rests on.

1. 1

   Collect field evidence

   Source

   Take imagery and condition data from the field.

2. 2

   Diagnose

   Observed

   Assess what the imagery shows, with the frame attached.

3. 3

   Compare to history

   Observed

   Read the observation against the season so far.

4. 4

   Recommend

   Recommendation

   Produce irrigation and treatment recommendations with basis and confidence.

Example use cases

Each example has its own page you can read and share.

Ask CropYield

Illustrative examples. Results depend on your own data.

• “What is affecting this section of the field?”
• “How much irrigation does this block need this week?”
• “What has changed here since the last flight?”
• “Which blocks should I walk first?”

Security, governance and human control

Where the software deliberately limits itself.

Questions

Does it replace an agronomist?

No. It produces diagnoses and recommendations with visible evidence so an agronomist can act or overrule quickly.