IoT Sensor Network for Lion's Mane, Kale and Spinach

A small-scale IoT sensor network to monitor and optimize growing conditions for lion's mane mushrooms, kale and spinach, with real-time data tracking and alerts.

ESP32Raspberry PiNode.jsMongoDBReactNext.jsAWS IoT CoreGrafanaTimescaleDB

A compact IoT sensor network designed to monitor environmental conditions for lion's mane mushrooms, kale and spinach. This project tests the feasibility of smart farming by tracking temperature, humidity, light and soil moisture, with data accessible via a web dashboard and automated alerts for suboptimal conditions.

Features

Real-Time Monitoring: Tracks temperature, humidity, light and soil moisture for lion's mane (15–22°C, 85–95% humidity, low light) and kale/spinach (18–24°C, 60–70% humidity, high light).
Web Dashboard: Visualises live and historical data using a React interface.
Automated Alerts: Sends notifications (email/SMS) when conditions deviate from optimal ranges.
Manual Control: Relay controls a fan (mushrooms) or water pump (greens).
Scalable Design: Modular setup supports additional sensors or crops.
Low-Cost: Built with affordable components (< $100).

Implementation

The project uses an Arduino Uno (or ESP8266 for Wi-Fi) to collect data from a DHT22 (temperature/humidity), photoresistor (light) and capacitive soil moisture sensor. A 5V relay controls a fan or pump. Node.js with Express powers the backend, MongoDB stores data and AWS IoT Core enables cloud connectivity. A React dashboard displays data. The setup runs on USB power for testing, with plans for solar scalability.

Two grow zones are configured: one for lion's mane (high humidity, low light) and one for kale/spinach (moderate humidity, high light). Data is logged locally (SD card) or sent to AWS for remote access.

Challenges

Sensor Calibration: Fine-tuned DHT22 for high humidity in the mushroom zone using manual gauges.
Connectivity: Wi-Fi reliability issues mitigated with offline SD card logging and retry logic.
Power: USB power limits scalability; solar integration planned for future iterations.
Cost vs. Performance: Balanced affordability with reliability through modular design.

Feasibility Testing Goals

Confirm accurate monitoring for lion's mane (85–95% humidity, low light) and kale/spinach (60–70% humidity, 6+ hours light).
Verify alerts and actuator response (fan/pump) to real-time data.
Evaluate scalability by testing sensor additions.
Keep prototype costs under $100.

Scaling Up

Add CO₂/pH sensors or Raspberry Pi for advanced processing.
Integrate solar power (10W panel, battery) for sustainability.
Enhance dashboard with machine learning for yield prediction or weather API integration.
Expand to multiple grow zones or a full greenhouse with AWS IoT Core.

Getting Started

Hardware: Buy Arduino Uno ($25), DHT22 ($5), photoresistor ($2), soil moisture sensor ($5), relay ($5). Set up in a grow box with lion's mane spawn and kale/spinach seedlings.
Software: Clone repo (placeholder: iot-crop-monitor), install Node.js/MongoDB/React, flash Arduino firmware.
Test: Monitor for 2–4 weeks, adjust based on alerts/dashboard.

Considerations for Scalability

Real-time monitoring of temperature (18–24°C), humidity (60–70%), light (6+ hours) and soil moisture.
Next.js dashboard with Grafana for analytics.
Automated irrigation, lighting and climate control via PLCs.
LoRaWAN for long-range, low-power connectivity.
Solar-powered with battery storage for off-grid operation.
Machine learning for yield prediction.

Implementation

Uses Raspberry Pi for central processing, ESP32 for sensor nodes and BME680/TSL2591/VH400 sensors. Node.js with FastAPI and TimescaleDB manages data, hosted on AWS IoT Core with Kubernetes. LoRaWAN ensures connectivity. Hydroponics and LEDs optimise growth, powered by 500W solar arrays.

Challenges

Connectivity in rural areas mitigated with LoRaWAN/5G hybrid.
Sensor calibration automated via software.
Data overload managed with edge computing and TimescaleDB.
Security enforced with TLS and zero-trust policies.

Scaling Strategy

Add sensor nodes and gateways for larger farms.
Integrate TensorFlow Lite for predictive analytics.
Expand solar capacity for full off-grid operation.

Iteration 2: Precision IoT Crop Monitoring System

Building on the insights from the initial prototype, Iteration 2 upgrades critical components to improve reliability, accuracy and scalability. This version replaces suboptimal sensors like the DHT22 and photoresistor with industrial-grade alternatives, integrates more powerful microcontrollers and expands cloud capabilities for long-term feasibility testing.

Key Improvements

High-Accuracy Sensors:
- Replaced DHT22 with Sensirion SHT45 (±0.1°C, ±1% RH).
- Replaced photoresistor with TSL2591 (up to 88,000 lux).
- Replaced capacitive moisture sensor with Vegetronix VH400.
- Optional: MH-Z19B CO₂ sensor and DFRobot analog pH sensor.
Microcontroller Upgrade:
- Moved to ESP32 (integrated Wi-Fi, deep sleep, ADC).
- Optional central node: Raspberry Pi 4 for data logging and edge processing.
Improved Architecture:
- Sensor nodes send data to a Pi gateway and onward to AWS IoT Core.
- Real-time alerting and data backup with TimescaleDB and Grafana.
- Offline logic for local actuator control using ESP32 or Pi.
Dashboard Upgrade:
- Next.js + Grafana dashboard with responsive UI, live and historical data, zone controls and actuator status.
Power:
- Outdoor zones run on 10W solar + battery.
- Indoor testing continues with USB/AC power.