CropLens is an offline AI crop-disease detection app built for tribal women smallholder farmers across Northeast India — no signal required, no literacy assumed, no delay between symptom and advice.
In the hill districts of Meghalaya and across Northeast India, three barriers compound at once.
Cloud-based diagnosis tools fail where mobile data doesn't reach — exactly where smallholder farms are.
Text-first apps exclude farmers who read little English or Hindi but speak Khasi or Garo fluently.
By the time a sample reaches an extension officer, the disease has often already spread across the plot.
Every feature answers one constraint a Khasi Hills farmer actually faces.
Runs a MobileNetV2 model converted to TensorFlow Lite directly on-device, so a diagnosis doesn't wait for signal.
Results and treatment guidance are spoken back aloud (currently in English), removing the literacy barrier entirely.
Where even the app can't run, a lightweight SMS channel keeps the advisory loop alive.
Trained local champions carry the tool into the community, closing the trust gap that pure tech can't.
The farmer or a Village Champion photographs the affected leaf using the phone's camera — no upload needed.
The on-device MobileNetV2 / TFLite model classifies the disease locally, entirely offline.
The result is delivered back as a spoken audio advisory — or by SMS where the app itself can't run.
This runs the trained MobileNetV2 model directly in your browser — no server, no upload leaves your device.
Upload a leaf photo to see a sample diagnosis appear here.
Chosen specifically to run on low-end, offline Android devices.
The current prototype proves the core idea works. These are the planned next steps.
Initial CropLens concept and application submitted.
Refined proposal document and a separate participant details submission, revised through several passes on word limits, originality, and claim strength.
Stage III materials completed and submitted. Result pending from the FAO team.
ICAR RC NEH Region, Umiam — leading the CropLens application.
Partnering on the CropLens submission.
Senior Scientist, DCS, ICAR RC NEH — faculty endorsement for the project.
For collaboration, endorsement, or questions about the FAO submission — reach out directly.