EcoRoot

Aiding afforestation through smart monitoring and alert systems

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Inspiration

My inspiration came from our nursery farm at home and the challenges we faced. After coming up with the idea, I realized it could also play a major role in addressing climate change.

What it does

The system monitors soil moisture and light levels in real time using an ESP32. It displays the readings on an OLED screen, sends WhatsApp alerts for critical conditions, and logs data remotely to ThingSpeak for visualization. This ensures that the plants get the right amount of water and light, while also allowing remote monitoring from anywhere.

How we built it

The project was built with an ESP32 microcontroller, sensors (soil moisture, light, and DHT11), and an OLED display. The ESP32 connects to WiFi, sends alerts via the CallMeBot WhatsApp API, and uploads data to ThingSpeak for remote logging and graph visualization.

Challenges we ran into

My biggest challenge was doing most of the work entirely on my phone — from simulating and coding to uploading to the board and committing to GitHub. It was a wonderful but tough experience. Other challenges came from handling the physical wiring and connections of the components.

Accomplishments that we're proud of

  • Successfully implementing remote logging with real-time graphs on ThingSpeak
  • Building a WhatsApp alert system for critical conditions
  • Going this far without being very experienced in C++ programming

What we learned

I learned a lot about APIs, C++ syntax, serial communication, and HTTP requests. Along the way, I also picked up smaller but very useful skills that strengthened my understanding of IoT systems.

What's next for EcoRoot

This project is still in its early stages, but I’ve realized it could be applied more broadly in agriculture. There are two possible paths forward:

  1. Integration with actuation systems (like automated watering). This would need more resources and has already been implemented in some solutions.
  2. A software-driven AI system that bridges the knowledge gap by giving plant-specific suggestions. With more sensors, it could provide accurate advice tailored to different crops, making it more impactful.

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