Agricultural advisory and regional pest & disease early-warning for smallholder farmers in Kenya — delivered over any phone, in the farmer's own language.
On Telegram and WhatsApp, a farmer can send a photo of a sick plant. Most tools would return a raw image-classifier score and stop there. AgroFutures doesn't.
It checks the photo against what it already knows about that farm — growth stage, vapour-pressure deficit, recent weather, soil. When the classifier's read doesn't fit those conditions, it visibly revises or hedges its own answer and asks for more evidence, rather than forcing a false diagnosis in either direction.
That's the reasoning discipline the whole platform is built on: confirm, discard, or flag — never silently trust a score, never silently reject an out-of-pattern signal. It's a real, demonstrable behaviour, shown in the recording, not a claim.
Telegram / WhatsApp photo diagnosis — recording to be embedded.
Seven thousand languages are spoken on earth; the major AI platforms were trained on a fraction. We use Khaya AI (GhanaNLP) for African-language TTS and are integrating PazaBench ASR models from Microsoft Research Africa for voice input in Dholuo, Kalenjin, Kikuyu, Maasai, and Somali. Language is not a localisation problem. It is an access problem.