AURORA

Inspiration

AURORA was inspired by the need for smarter, more sustainable greenhouse management. Traditional greenhouse farming often depends on manual monitoring of temperature, humidity, soil moisture, and light conditions, which can lead to delayed actions and inconsistent plant growth. We wanted to build an affordable IoT-based system that continuously observes the greenhouse environment and helps farmers make timely decisions. The idea also focuses on improving pollination by identifying suitable environmental conditions and activating a vibration mechanism when required.

What it does

AURORA is an IoT-powered smart greenhouse monitoring and pollination system. An ESP32 collects real-time data from sensors measuring temperature, humidity, soil moisture, and light intensity. The system evaluates whether the environmental conditions are suitable for plant growth and pollination. A vibration motor can be activated to assist pollination when the required conditions are satisfied. The collected data is stored in Supabase and displayed through a web dashboard, allowing users to monitor greenhouse conditions remotely and understand changes over time.

How we built it

The system was built around an ESP32 microcontroller connected to environmental sensors such as a DHT sensor for temperature and humidity, a soil moisture sensor, and an LDR for light detection. The ESP32 processes the sensor readings and determines the current greenhouse and pollination status. A vibration motor is integrated as the pollination-support mechanism. For cloud storage and real-time data management, we used Supabase with a structured greenhouse data table. A web-based dashboard was developed to present sensor readings, environmental status, historical data, and pollination conditions in an accessible format.

Challenges we ran into

One of the main challenges was obtaining reliable sensor readings under different environmental conditions. Soil moisture and light sensors can produce fluctuating values, requiring suitable threshold selection and status classification. Integrating the ESP32 with the cloud database was another challenge, particularly in ensuring that data was correctly transmitted and stored. We also had to coordinate multiple sensor inputs so that the pollination mechanism would operate only when the required conditions were satisfied. Developing a dashboard that clearly presents continuously changing sensor data while remaining simple for users was an additional challenge.

Accomplishments that we're proud of

We successfully developed a working prototype that combines IoT sensing, cloud data storage, greenhouse monitoring, and automated pollination support in a single system. The ESP32 can collect multiple environmental parameters and evaluate greenhouse conditions in real time. We also integrated Supabase for storing sensor data and created a dashboard for remote visualization. Another achievement is the implementation of condition-based pollination logic, which makes the system more than just a monitoring platform and moves it toward intelligent greenhouse automation.

What we learned

Through AURORA, we learned how hardware, software, cloud services, and automation can be combined to solve a real-world agricultural problem. We gained practical experience with ESP32 programming, sensor integration, threshold-based decision-making, IoT communication, database management, and dashboard development. We also learned the importance of handling unreliable sensor values, designing clear user interfaces, and testing an IoT system under different environmental conditions. Most importantly, we understood how real-time data can be converted into useful actions rather than simply being collected and displayed.

What's next for AURORA

The next version of AURORA can introduce AI and machine learning to move from rule-based monitoring toward predictive greenhouse management. The system could predict unsuitable environmental conditions, recommend irrigation and lighting requirements, and identify the best time for pollination based on historical sensor patterns. Camera-based computer vision could also be added to detect flowers, monitor plant growth, and identify pollination progress. Future versions could include automatic irrigation, ventilation, lighting control, mobile notifications, multi-greenhouse support, and an AI assistant for farmers. These improvements would transform AURORA into a complete intelligent greenhouse platform focused on productivity, resource efficiency, and sustainable agriculture.

Built With

• agriculture
• agritech
• ai
• automation
• cloud-computing
• data-analytics
• dht22
• edge-computing
• embedded-systems
• environmental
• esp32
• iot
• ldr-sensor
• machine-learning
• pollination
• precision-agriculture
• real-time-monitoring
• sensors
• smart-farming
• smart-greenhouse
• soil-moisture-sensor
• supabase
• sustainable-agriculture
• vibration-motor
• web-dashboard