Agriculture · Crop disease detection
Banao trains computer-vision models that read leaf, canopy, and fruit images from field cameras, drones, or a scout's phone — and flag disease or pest pressure within hours of capture, not days after a field visit.
The model grades by block, not by whole-farm impression, so a treatment decision covers the affected area rather than a precautionary spray across every acre. It runs on images already being captured on farms that have cameras; farms without them start with a scout-app integration.
CP Plus— existing farm camera network repurposed to detect early-stage crop stress events without additional hardware.
A detection system is the model plus the image pipeline, the alert layer, and the field-team workflow. We own all three.
We establish an image-collection workflow — drone flight plan, fixed camera schedule, or scout-app protocol — and label the resulting images to your agronomist's grading standard, not a generic plant-pathology dataset.
A vision model trained on your specific crops, your region's common pathogens, and your actual field conditions. Output is disease class, severity estimate, and affected block — not a confidence score no one knows what to do with.
When the model flags an event, an alert goes to the field manager with GPS block coordinates and a sample of the flagged images. The farm head sees where to send a scout, not just that something is wrong.
Agronomists capture images in the field on a purpose-built app; the model scores them before the scout finishes the walk. Drone integration processes each flight batch as it lands.
Model output connects to your crop-advisory or ERP system so the detected disease class surfaces the relevant treatment protocol, dosing rate, and pre-harvest interval alongside the alert — no separate lookup.
Agronomist corrections and confirmed outbreak records feed back, so the model improves each season instead of drifting as new pathogen strains appear or crop varieties change.
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified against ground truth.
CP Plus
The farm had cameras for security. Banao added an inference layer that reads the same feeds for early crop stress — leaf discolouration, canopy texture change, unusual bare patches — and sends a block-level alert before the event spreads to neighbouring rows.
Banao operates a ~300-person engineering company on its own AI products. InterviewGod screens every engineering hire we make; Vikaas runs our own demand-generation pipeline. Both are products we also sell — which means they have to survive our own operation first.
A crop disease model that reaches a farm has already gone through the same discipline: proven in a controlled context, stress-tested against real variation, then handed to an operator who has to depend on it. That is the standard we hold every deployment to.
We will tell you before you spend on a build:
We do not quote a detection system off a generic crop catalogue. We run your actual images through a baseline model before anything is scoped.
We collect a sample of your existing field images, run them through a baseline detection model for your key disease classes, and hand back an accuracy estimate and a feasibility assessment — yours to keep regardless of what you decide next. If you proceed, the Sprint cost is credited against the build.
Full labelling, model training to your agronomist's grading standard, and integration with your image-capture workflow, alert channel, and crop-advisory system.
Deployed to your farms with agronomist-override, block-level mapping, and a seasonal retraining cycle so the model keeps pace with new strains and variety changes.
What diseases and crops can the model identify?
Any crop disease or pest signature that a camera can see — blights, mildews, rusts, mosaic patterns, lesions, discolouration, and pest-pressure signs. The model is trained on your specific crops and the pathogen classes relevant to your geography, not a generic global dataset.
Do we need drones, or will phone images work?
Either works. Scout-app images from a phone are the lowest-friction start — the agronomist photographs suspect plants, the model returns a call before the scout leaves the block. Drone integration adds coverage for fields too large for a ground scout. The Discovery Sprint establishes which capture method fits your operation.
How much labelled data do we need before training?
Enough to cover your real disease classes at their different severity stages — typically a few hundred confirmed examples per class to start, supplemented by augmentation and public datasets where your pathogen set overlaps them. The Sprint establishes whether your existing records and field photos are sufficient or whether a structured collection campaign is needed.
How quickly does the model return a result after an image is captured?
Scout-app captures are scored in seconds; drone-batch processing completes within minutes of a flight landing. The latency is well within any treatment window — the gap the model closes is not the seconds between capture and score, it is the days or weeks between outbreaks and the next scheduled field visit.
Will this integrate with our farm-management or ERP platform?
Yes. Alert output and disease classifications connect to the systems your agronomy and operations teams already work in — farm-management software, WhatsApp or SMS channels, ERP procurement modules, or a purpose-built dashboard. We scope the integration in the Discovery Sprint and deliver it as part of the build.
Share a batch of your existing field photos or drone footage. In 45 minutes we will walk through what early detection is possible on your actual crops and what it would cost to build.
Book a 45-min scoping call