DriMon is a comprehensive greenhouse monitoring system developed by Fredrik Rødland that combines ESP32-based sensor hardware with a web-based visualization platform. The system provides real-time and historical data about greenhouse conditions at Rødtangen, Norway, enabling smart monitoring and analysis of plant growth environments.
- Live Dashboard - Real-time charts and data visualization
- ThingSpeak Channel 1 - Main environmental data
- ThingSpeak Channel 2 - Plant monitoring data
- ThingSpeak Channel 3 - System performance data
- Multi-point Temperature: Ambient, soil, and plant-specific measurements
- Climate Tracking: Humidity, air pressure, and light intensity
- Soil Monitoring: Multiple moisture sensors for different plants
- Window Status: Distance-based opening detection
- Weather Integration: YR.no forecasts with comparative analysis
- Interactive Charts: Time-series data with statistical analysis
- Multi-series Visualization: Combined data from related sensors
- Statistical Indicators: Min/max/average calculations with visual markers
- Smart Date Ranges: Customizable time periods with chart-specific defaults
- Cross-chart Synchronization: Coordinated tooltips across related measurements
- Solar Powered: Energy-efficient monitoring with battery backup
- Responsive Design: Optimized for desktop, tablet, and mobile devices
- Multi-language Support: Norwegian, English, and Spanish translations
- Theme Options: Light/dark mode with system preference detection
- Offline Capabilities: Local caching and connectivity monitoring
The system uses ESP32 microcontrollers with a comprehensive array of sensors:
- Environmental Sensors: BME280 (temperature, humidity, pressure), AHT20 (precision temp/humidity)
- Light Measurement: BH1750 sensors for ceiling and internal light levels
- Soil Monitoring: Capacitive moisture sensors, DS18B20 temperature probes
- Position Sensing: VL53L0X Time-of-Flight distance sensor for window opening
- Power Management: Solar charging with LiPo battery and fuel gauge monitoring
- Local Display: OLED and LCD screens for on-site readings
All components are integrated on a custom PCB with optimized power management for long-term operation.
- Project Overview - Complete system description with hardware and software details
- Development Guide - Architecture, components, and code organization
- Web Application Documentation - Complete web dashboard technical documentation
- Future Enhancements - Completed features and planned improvements
The web interface is a sophisticated single-page application built with vanilla JavaScript and Chart.js. It features:
- 34 JavaScript modules organized in a component-based architecture
- Multi-language support (Norwegian, English, Spanish) with dynamic loading
- Responsive design optimized for desktop, mobile, and dashboard modes
- Real-time data visualization with statistical analysis and interactive tooltips
- Advanced integrations including weather APIs and astronomical calculations
- Smart caching with performance optimization and offline capabilities
Live Dashboard: https://drimon.rodland.no/
The web interface can be previewed locally using any static file server:
# Using Python
python -m http.server
# Or using Node.js
npx serve docs/For complete technical documentation including all JavaScript modules, URL parameters, view modes, and API integrations, see the Web Application Documentation.
The Arduino code for the ESP32 is organized into multiple files:
20240724_drimon_1_3.ino: Main entry point and initialization3_setup.ino: Sensor and connectivity setup4_met.ino: Weather API integration5_measure.ino: Sensor measurement routines7_display.ino: OLED and LCD display handling8_sleep.ino: Power management and deep sleep9_thingspeak.ino: Data transmission to cloud storage9_util.ino: utility functionssecrets.h: keys to wifi and thingspeaksensordata.h: Data structure definitions
Before compiling the Arduino code, you need to create a secrets.h file in the same directory with your specific configuration values:
#ifndef SECRETS_H
#define SECRETS_H
// WiFi credentials
#define WIFI_SSID "YourWiFiNetworkName"
#define WIFI_PASSWORD "YourWiFiPassword"
// ThingSpeak Channel 1 - Main environmental data
#define THINGSPEAK_1_CHANNEL 2568299
#define THINGSPEAK_1_API "YOUR_16_CHAR_API_KEY"
// ThingSpeak Channel 2 - Plant monitoring data
#define THINGSPEAK_2_CHANNEL 2584548
#define THINGSPEAK_2_API "YOUR_16_CHAR_API_KEY"
// ThingSpeak Channel 3 - System performance data
#define THINGSPEAK_3_CHANNEL 2584547
#define THINGSPEAK_3_API "YOUR_16_CHAR_API_KEY"
#endifReplace the placeholder values with:
- Your WiFi network name and password
- Your ThingSpeak write API keys (16-character strings from your ThingSpeak account)
The channel numbers are already configured for the DriMon system's public channels.
The DriMon system consists of three main components:
ESP32-based monitoring stations with multiple sensors, solar power, and local displays. The firmware implements sophisticated power management with adaptive sleep cycles based on light conditions.
ThingSpeak channels organize the data into logical groups:
- Channel 1: Main environmental readings
- Channel 2: Plant-specific monitoring
- Channel 3: System performance metrics
A modern, responsive web interface built with:
- Chart.js: Interactive data visualization
- Vanilla JavaScript: Component-based architecture with no framework dependencies
- Responsive Design: Optimized for all device sizes
- Internationalization: Custom i18n system
The system implements sophisticated power conservation:
- Solar Charging: Primary power source with 5V solar panel
- Battery Backup: 3.7V LiPo with charge level monitoring
- Adaptive Sleep: Variable cycles based on light conditions
- Night: 900 second intervals
- Dusk/Dawn: 420 second intervals
- Day: 600 second intervals
- Sensor Power Control: Selective power to sensors to minimize consumption
The dashboard provides advanced data analysis features:
- Statistical Calculations: Min/max/average values for all measurements
- Visual Indicators: Statistical markers on charts
- Time Range Selection: Customizable periods from 24 hours to 30 days
- Synchronized Tooltips: Related measurements shown together
- Weather Comparison: Indoor vs. outdoor conditions
Planned enhancements include:
- Webcam integration with time-lapse capabilities
- Automated watering system based on soil moisture
- Advanced analytics for plant growth optimization
- Mobile app with push notifications
- Machine learning for predictive maintenance
For a complete list of planned features, see the Future Enhancements document.
This project is open source and available under the terms specified in the LICENSE file.