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GreenHouse: An Apple Home Greenhouse Controller
I want to share my hobby project: a greenhouse controller that connects to the Apple Home ecosystem. It is a pilot version that currently supports temperature sensors, but it is already fully capable of standalone operation.
What is working now
Hardware core: The device is built on the LilyGO T-A7608E-H board (ESP32) with a GSM modem.
Sensor support: It collects data from DS18B20 temperature sensors and can handle up to 10 of them.
Data delivery: It sends all data via MQTT to a home server.
Power saving: It uses an 18650 battery and enters a deep sleep mode to save energy.
Smart intervals: The sleep timer adapts automatically, waiting 2 minutes if a send fails and 15 minutes if it succeeds.
Server side: A Python-based logger tracks temperatures, while a web interface built with Flask and Chart.js displays live charts.
Apple Home integration: HomeBridge and the MQTTThing plugin bring everything into your Apple Home app automatically.
Future plans
This project was built from the ground up to be an expandable platform. In the future, I plan to add automated watering, lighting controls, and ventilation management. Right now, it is a fully functional, DIY temperature monitoring tool that you can build yourself.
Links
Web Demo: https://greenhouse.dronetales.net
I would love to hear your feedback, ideas, and suggestions for future updates!
github.com
GitHub - DroneTales/GreenHouse: Контроллер теплицы для Apple Home
Контроллер теплицы для Apple Home. Contribute to DroneTales/GreenHouse development by creating an account on GitHub.
The adaptive sleep intervals are a lovely detail—small power-aware decisions like that make the hardware feel genuinely considered, not just functional. Curious how you’re thinking about presenting the sensor history in the home-server UI.
I use Apple Home, so unfortunately, viewing the history directly from the Home app is not available. However, for this exact purpose (viewing the history), I created a Python web app that allows you to check the history for any period via a web interface. (The link is in the...
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React • TypeScript • Node.js • Express • MongoDB Atlas • Google Gemini AI • REST APIs • JWT Authentication • Tailwind CSS • Vercel
This project has strengthened my skills in Full-Stack Development, AI Integration, API Development, UI/UX Design, Authentication, and Scalable Application Development. More features and AI-powered improvements are already in progress.
I'm always eager to build impactful products and solve real-world problems through technology.
💼 Open to Opportunities
I'm currently open to Freelance projects, Software Development Internships, and Full-Time Software Engineer roles where I can contribute, learn, and build innovative AI-powered applications.
I'd love to hear your feedback on Arogya Raksha. If you know of any opportunities or think we'd be a good fit to collaborate, feel free to connect or reach out!
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Your scenario is green. Three of its branches have never run.
Developers get unit tests, staging and CI. Everyone shipping on Make, n8n and Zapier gets a toggle. You turn the scenario on and watch it for a week.
That only ever finds the loud failures - a step errors, the platform goes red, you get an email. The expensive ones are silent: the run that passes every step and does the wrong thing.
I built a simple radar + dashboard in about 40 minutes.
I had a servo motor, ultrasonic sensor, and an ESP32 sitting around, so I got curious about what I could make with them.
The setup is pretty simple: the servo sweeps the sensor, the ESP32 reads the distance, and that data is sent to a small web dashboard that visualizes it in real time.
This little experiment got me into microcontrollers a few months ago. Since then, I've built an outdoor smart pot and an autonomous robot.
I'll be sharing more of that IoT journey in the next few posts.
Starting with the radar.
Github Repo
This is a great example of a fast hardware prototype becoming a readable interface—the radar view and live servo setup make the feedback loop tangible. The 40-minute constraint clearly kept the build focused on the core interaction.