AI-Ready Data Model
Field images, device metadata, timestamps, and sensor values can be sent to backend services for disease detection, prediction models, and pattern analysis.
Technology Stack
A practical agri-tech stack built for data capture, automation, and future AI expansion.
SmartAgroNet is designed around a clear hardware-to-software pipeline that supports image capture, sensor reading, relay control, dashboard analytics, and external API-driven intelligence.
| Layer | Technology | Purpose |
|---|---|---|
| Edge Device | Raspberry Pi 3B+ | Runs scheduled jobs, captures sensor values, sends API data, and controls relays. |
| Visual Detection | Raspberry Pi Camera Module | Captures crop images for plant disease analysis and remote inspections. |
| Field Inputs | Soil, weather, water, and NPK sensors | Provides field-level readings for automation and analytics. |
| Control Layer | Relay modules and actuators | Switches pumps, mist makers, or irrigation valves based on platform rules. |
| Backend | Laravel API | Handles storage, authentication, alerting, business logic, and AI integration points. |
| Frontend | Web dashboard | Shows farm health, sensor history, alerts, and operational status. |
Scheduled Execution
Support hourly image capture and sensor sync tasks using cron jobs or Linux service scheduling on Raspberry Pi.
Automation Control
Backend-generated actions can instruct the device layer to trigger pumps, irrigation relays, and smart alerts.
Scalable Approach
Start with one land and scale to many
The stack can evolve from a single Raspberry Pi pilot to a multi-land deployment where every site reports into a central Laravel platform.