CropSense AI
A field-scouting app that lets scouts photograph a crop problem and receive a ranked list of probable causes with confidence levels and tailored next steps. When confidence is low, cases are routed to an agronomist for expert review.
The problem
Agronomists and scouts at co-operatives and advisory services face growing pressure to diagnose crop problems quickly across many farms. Hand-written or informal scouting notes vary in quality, making it difficult to act decisively. Without confident diagnosis, advisors recommend treatments defensively, leading to unnecessary chemical use and wasted budget. The shortage of experienced agronomists grows each season. Patterns of disease or pest pressure across a service area remain hidden because observations are scattered or never collected. This cascades into slower response, missed opportunities to contain outbreaks early, and difficulty meeting sustainability targets that demand more precise, less chemical-intensive decision-making.
The product
CropSense AI is a field-scouting application that captures photographs of crop problems with GPS location and growth-stage metadata. A computer-vision diagnosis engine runs predictions and returns ranked probable causes with confidence scores, automatically routing low-confidence cases to your expert agronomists for review. Scouts work offline and sync observations when a connection is available. Treatment guidance is authored and maintained by your own qualified agronomists, ensuring every recommendation reflects your expertise and liability protection. An outbreak map aggregates anonymised observations across your service area so advisory teams and growers can see where disease or pest pressure is building. The system tracks outcomes to support continuous model learning.
What it does
Scouting capture app
The app captures photographs with GPS location and crop growth-stage information, queuing observations offline until a connection is available.
Diagnosis engine (pilot crops)
A computer-vision model diagnoses the crops and diseases you define, providing ranked predictions with confidence scores for each case.
Advisory content
Treatment guidance for each finding is authored and maintained by you, with version control to track all revisions.
Agronomist review queue
Cases where the model has low confidence are automatically routed to your expert agronomists, who can communicate directly with the farmer through in-app chat.
On-device inference
A compressed machine-learning model can run on the device itself, allowing diagnosis even without network connectivity.
Outbreak map
Anonymised observations from all scouts are aggregated into a visual heatmap showing where disease or pest pressure is concentrated across your service area.
Problems it solves
- Finding enough experienced agronomists to diagnose crop problems quickly becomes harder each season.
- Hand-written or informal scouting notes vary in quality and detail, making it difficult to act on observations or track patterns.
- Without confidence in a diagnosis, advisors recommend treatments defensively, leading to unnecessary chemical use and wasted budget.
- You cannot see where disease or pest pressure is building across your service area because data is scattered or unavailable.
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