The problem
Smallholder farmers often notice a disease only after it has spread, and the nearest agronomist can be a day away. Rural networks are patchy, so cloud-only diagnosis tools fail exactly when they are needed.
What to build
Build a phone app that identifies common crop diseases from a leaf photo using an on-device model, explains treatment options in the farmer's language and queues the case for expert review when connectivity returns.
Success looks like
At least 85% top-3 accuracy on a held-out set of locally collected images, results in under 3 seconds with no network, and a feedback loop that flags low-confidence cases.
Suggested technologies
- On-device ML
- TensorFlow Lite
- Image classification
- Android
- Offline-first sync
An original, independent statement. It is not an official Smart India Hackathon problem statement, and Arventiq Labs is not affiliated with or endorsed by that programme. The scenario is illustrative and does not describe any specific organisation. Free to use with credit.