Industries · Agriculture
Agricultural AI depends on crops, pests, soils and practices that are intensely local.
A disease-detection or advisory model trained on other regions misidentifies local crops and pests, and gives advice that does not fit the season, the soil or the smallholder economics of the farm receiving it.
Where it breaks
Common failure modes.
- Crop varieties and pests absent from training data
- Advice that ignores local seasons, inputs and costs
- Imagery captured under different conditions and equipment
- Guidance delivered in a language the farmer does not use
What we do
How we work in agriculture.
01
Field imagery
Crop, pest and soil imagery captured in real conditions.
02
Local knowledge
Practices and constraints documented by people who farm there.
03
Advisory evaluation
Whether guidance is correct and actionable for that farm.
04
Language coverage
Advisory content in the languages farmers actually speak.
Other industries
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