Vision Engine
Yield estimation, built from the season - not a single day
A one-off snapshot cannot tell you what a field will produce. Agrofuze builds a yield approximation from field-level imagery gathered across the growing season, watching how canopy, health and stress evolve.
How it feeds the estimate
Season imagery in, an approximation out
01
Field imagery
Field-section photos captured repeatedly through the season.
02
Growth over time
Canopy development and health trend tracked across each capture.
03
Stress weighting
Detected disease and deficiency reduce the projected potential.
04
Approximation
A yield range for planning, with the uncertainty stated alongside.
Stress zone
Healthy rows
Scanning Field sectionConfidence 81%
78%
Accuracy caveats
An approximation, stated as one
Yield estimation is the hardest thing the engine does, and the caveats are part of the output, not the fine print.
- Results are a range, not a single guaranteed number.
- Weather, soil and late-season events the camera cannot see all move the real figure.
- Sparse or inconsistent imagery through the season widens the uncertainty band.
- Best used for relative planning between fields, not as a contractual figure.
Yield trends across many fields
For cooperatives and agri-businesses, estimates roll up into aggregated trends for planning at scale.
For agri-businesses