How I Built a Intelligent AgTech Risk Monitoring System: Architecture, Technical Decisions, and Key Learnings

Source: Dev.to

Introduction

This project was developed as part of the Hardware Architecture course at my university. Our team set out to build a simple yet powerful system where we could experiment with different technologies, sensors, and hardware components. It was our first hands‑on experience working with sensors and embedded systems, so we aimed to create a solution that was fast, scalable, and accessible.

In this article I walk through the project’s architecture, the technologies used, the challenges we faced, and the key lessons learned throughout the development process.

Main Features

• Real‑time monitoring – Continuously collects environmental data (temperature, humidity, and luminosity) from sensors connected to an Arduino.
• Asynchronous data pipeline – Uses a message queue (RabbitMQ) to reliably transmit sensor readings for analysis and storage.
• Risk analysis engine – Processes sensor data to compute risk levels for pest outbreaks, with multi‑tier alert levels.
• Dashboard interface – Interactive web dashboard built with Next.js that displays real‑time and historical visualizations.
• Scalable architecture – Designed with distributed components that can scale independently and adapt to multiple crop types.

Tech Stack

Hardware

• Arduino Uno with environmental sensors
  • DHT11 (temperature & humidity)
  • HW080 (humidity)
  • LDR (luminosity)
• Raspberry Pi – hosts backend services

Backend

• Python 3.8+ (Flask)
• RabbitMQ (CloudAMQP) – asynchronous messaging
• Redis (Upstash) – near real‑time data caching
• SQLite – historical data storage
• PySerial – Arduino communication
• Pandas – data analysis

Frontend

• Next.js (React) with TypeScript
• Tailwind CSS & shadcn/ui – UI components
• Recharts – interactive data visualizations

Tools

• Git – version control
• VS Code – primary editor

Data Flow

Sensors → Arduino → Message Queue → Backend → Database → Frontend

Asynchronous Messaging

RabbitMQ decouples producers and consumers through asynchronous, message‑driven communication. This design lets system components operate independently, stay resilient to failures, and support multiple processing paths such as real‑time streaming, historical storage, and analytics. The same structure also enables future extensions (e.g., machine‑learning services) without changing the ingestion layer.

Separation of System Layers

The system is organized into three logical layers:

1. Hardware – data acquisition (sensors → Arduino).
2. Backend – processing, storage, and messaging.
3. Frontend – visualization and user interaction.

Separating concerns improves maintainability, simplifies testing, and allows each component to evolve and scale on its own.

Why RabbitMQ Instead of Direct HTTP?

Agricultural deployments often suffer from network latency, intermittent connectivity, and partial failures. RabbitMQ provides:

• Buffering – messages are stored until a consumer is ready.
• Reliable delivery – acknowledgments and retries prevent data loss.
• Asynchronous processing – downstream services can consume at their own pace.

These features ensure sensor data is never lost and can be processed even when parts of the system are temporarily unavailable.

Event‑Driven, ELT‑Oriented Data Pipeline

1. Extract – Arduino reads sensor values and sends them over a serial connection to the backend.
2. Load – The backend publishes the raw readings to RabbitMQ as events (no transformation yet).
3. Transform –
   • Raw data is routed to dedicated queues.
   • SQLite stores historical data; Redis caches recent readings for fast access.
   • Analysis services consume messages, compute derived metrics (e.g., pest‑risk indices), and publish results.
4. Consume – The Next.js frontend subscribes to the processed data and renders real‑time and historical dashboards.

Challenges and How We Addressed Them

Ensuring Reliable Sensor Data Ingestion

Sensor readings are generated continuously but hardware is prone to noise, temporary failures, and unstable communication.

Solution:

• Adopted asynchronous messaging with RabbitMQ.
• Sensor data is published as events, allowing buffering, retries, and independent processing.
• Decoupling acquisition from downstream services keeps the system operational even when individual components are temporarily unavailable.

Balancing Real‑Time Processing and Analytical Flexibility

We needed a solution that could support immediate visualization and future analytical workloads without major rewrites.

Solution:

• Preserve raw data in the ingestion layer.
• Separate consumption paths (real‑time dashboards vs. batch analytics).
• New consumers (e.g., predictive models) can be added without disrupting existing pipelines.

The final system successfully integrated hardware sensors, asynchronous ingestion, backend processing, and a web‑based dashboard into a single working solution. Sensor readings were collected, transmitted, processed, and visualized in near real time, while historical data remained available for retrospective analysis.

The project was demonstrated live: the complete data flow—from sensor acquisition to dashboard visualization—ran in real time, confirming the correctness of the integration and the architectural decisions made during development.

Future Work

Future iterations will focus on extending analytical capabilities and improving data‑processing quality. Planned steps include:

• Developing an initial predictive model to analyze historical sensor data and estimate the likelihood of pest or disease outbreaks.
• Enhancing the data pipeline to increase processing capacity, filter noisy sensor readings, and produce more reliable inputs for analysis and visualization.
• Integrating new sensors, refining alert mechanisms, and adapting the system for larger‑scale or more distributed deployments.

Conclusion

This project provided practical experience in designing and implementing a distributed, event‑driven system that integrates hardware, backend services, and a modern web interface.

Beyond the technical implementation, the project reinforced the importance of architectural decisions such as decoupling, data‑flow design, and system modularity. Working with real sensor data highlighted the challenges of handling data at the boundary between hardware and software.

Overall, the project served as a valuable learning experience in applied system design, bridging concepts from hardware architecture, data engineering, and web development.