At Robo, we love seeing young people use technology not only to learn, but to solve real problems. This project is a great example of that: a smart irrigation system for football fields, designed to support more efficient water use through real-time environmental data, satellite information, and machine learning.
Developed by a team of young innovators in Cyprus, the project shows how STEM skills can be applied to challenges that matter locally, from water conservation to the sustainable maintenance of public sports facilities.
The Challenge: Smarter Water Use for Football Fields
Football fields require regular irrigation to keep the grass healthy and playable. However, watering based only on fixed schedules can lead to unnecessary water consumption, especially when the actual condition of the soil and grass is not taken into account.
The aim of this project was to create a low-cost smart irrigation system that can help make watering decisions more data-driven. Instead of relying only on visual checks or routine watering, the system collects and analyses real measurements from the field and combines them with satellite-based indicators of vegetation health and moisture.
Building a Smart Irrigation System
To monitor the field conditions, the team used a combination of sensors. A Capacitive Soil Moisture Sensor v1.2 was used to measure the moisture level of the soil, while a DHT22 (AM2302) sensor was used to measure air temperature and relative humidity.
These measurements helped the team build a clearer picture of the environment around the grass. Soil moisture gives direct information about how much water is available in the ground, while temperature and humidity help describe the wider conditions that can affect evaporation and irrigation needs.
Combining Field Sensors with Satellite Data
One of the most interesting parts of the project was the use of satellite data from the Copernicus Programme. The team combined their sensor measurements with vegetation indices such as NDVI and NDMI.
NDVI, the Normalized Difference Vegetation Index, is commonly used to estimate the health and density of vegetation. In simple terms, it helps show how green and active the grass is. NDMI, the Normalized Difference Moisture Index, is related to vegetation moisture and can help identify areas where water stress may be present.
By combining ground-level sensor readings with satellite-based observations, the project moved beyond a simple sensor demo. It became a more complete environmental monitoring system, connecting local measurements with wider Earth observation data.
Using Machine Learning to Analyse Irrigation Needs
The data collected from the sensors and satellite sources was analysed using machine learning models, including Ridge Regression and XGBoost. These models were used to study patterns in the data and support predictions related to the condition of the field.
To evaluate the performance of the models, the team used Leave-One-Out Cross Validation (LOOCV), a method that tests how well a model performs when trained and evaluated across different data points. This helped the team assess the reliability of their predictions, even with limited experimental data.
Through this process, the team reported strong prediction performance and confirmed a meaningful relationship between soil moisture and the condition of the grass.
Testing the System in Real Conditions
The project was not limited to theory or a classroom demonstration. The team carried out an experimental application at the municipal football field in Latsia, where the system was installed and tested in real conditions.
During the field test, the team collected environmental data and examined how the soil moisture readings related to the actual condition of the turf. This practical testing gave the project real value, showing how the system could be used in a real sports facility rather than only as a prototype on a desk.
Supported by Experts and Mentors
The project was developed with the support of several important collaborators. The Eratosthenes Centre of Excellence, with the contribution of Despoina Makri and Marianna Hadjichristodoulou, guided the team in the analysis of satellite data.
Odysseas Economides from Robo supported the design and implementation of the sensor system, helping connect the environmental monitoring concept with practical electronics and hardware development.
In addition, Epiphanios Eustathiou contributed specialised knowledge in the field of irrigation, helping the team better understand the real-world requirements of water management.
A Low-Cost System with Real Potential
One of the strengths of the project is that it was designed as a low-cost system. This makes the idea especially interesting for municipalities, schools, sports organisations, and other groups that want to explore smarter ways to manage water use.
With further development, systems like this could support more sustainable irrigation practices not only for football fields, but also for parks, school gardens, public green areas, and other outdoor spaces.
What This Project Shows
This project is more than a technical achievement. It is a strong example of what young people in Cyprus can accomplish when they are given the opportunity to explore real problems through science, technology, engineering, and creativity.
By combining sensors, satellite data, and machine learning, the team created a project that connects multiple areas of modern technology. More importantly, they applied those tools to a challenge that is relevant to Cyprus and to the wider need for sustainable water management.
At Robo, we are proud to see young creators taking ideas beyond the classroom and into real-world applications. Projects like this remind us that innovation does not start only in large companies or research labs. It can start with curious students, the right guidance, and the confidence to build something meaningful.
Congratulations to the team for their excellent work. We hope this project inspires more young people in Cyprus to explore robotics, electronics, programming, data science, and environmental technology.
