Beyond flat-earth modelling
Traditional algorithms fail and hallucinate because they treat agricultural terrain as a flat surface.
Physics-aware corrections
The algorithm corrects machine learning errors by mapping coordinates to historically accurate ground truth from topographically identical hillsides, not just adjacent flatland.
That’s the difference between a plausible guess and a verified number.
Hybrid efficiency
Deep learning is combined with physical pedo-transfer functions, for example calculating organic carbon mathematically, to reduce computational cost and eliminate hallucination.
Commercial grade
The engine optimises for mean absolute error, exact proximity to lab results, and flags the areas that still require physical testing.