Crop Monitoring & Alert
Sensor-driven crop monitoring with AI anomaly detection — catch problems before they cost you.
What needed
solving
Farmers relied on daily physical inspection to detect irrigation failures, pest outbreaks, or nutrient deficiencies. Problems were often discovered too late, causing significant crop loss.
The automation steps
IoT sensors read soil moisture, pH, temperature, and humidity every 15 min
AI baseline model detects anomalies and predicts risk level
Field-specific alert sent to farm manager with diagnosis and recommended action
Incident logged; weekly summary report generated automatically
How the workflow runs
Each step fires automatically. Click "Simulate run" to see the sequence.
Click "Simulate run" to see the automation sequence step by step.
The numbers
Concrete, measurable outcomes delivered in production.
From brief to production
Discovery & scoping
We map the problem, understand the existing stack, and write a fixed-price proposal. No surprises.
1–3 DAYSBuild & weekly demos
You see working software every week. Feedback is incorporated immediately — not at the end.
2–4 WEEKSTesting & QA
Every integration is stress-tested before go-live. Edge cases handled, fallbacks in place.
3–5 DAYSGo live & handover
Full documentation, team training, monitoring setup, and 30-day post-launch support.
1 WEEKYou might also need
Build this for my business
Book a free 30-minute call. We'll scope your automation and tell you exactly what it would cost.