EECS BSc Student developed a new framework “FarmTechAI” for AI-driven modern farmer management system
Murat Can Cardak, a former Computer Science student at the School of Electronic Engineering and Computer Science, developed FarmTechAI, a pioneering AI-based farm management system, during his final year research project at QMUL.
Supervised by Dr. Sukhpal Singh Gill, Cardak’s project introduced a smart, integrated platform that equips farmers with real-time insights into crop health, weather conditions, and financial planning - all through an accessible, user-friendly dashboard. By combining artificial intelligence, machine learning, and Internet of Things (IoT) technologies, FarmTechAI supports smarter decision-making, sustainable resource use, and improved productivity.
Designed with accessibility in mind, the system collects data from weather services, crop sensors, and market forecasts to provide actionable recommendations on when to plant, irrigate, harvest, or adjust financial strategies. Its AI-driven approach not only helps increase yields and reduce waste but also lowers the barrier for small and medium farms to adopt advanced technologies.
Key features include:
- Integrated decision-making: Real-time synthesis of environmental and economic data
- Predictive AI tools: Machine learning models forecast optimal farming actions
- Open-source design: Code is freely available on GitHub, encouraging further innovation
- Community focus: Encourages data-sharing among farmers to enhance collective learning
The project contributes to the growing field of Artificial Intelligence of Things and serves as a real-world case study in applying AI, cloud computing, and IoT in agriculture. It also highlights the value of open-source development in driving technological progress.
Future developments include real-world trials in partnership with agricultural institutions, expanding support for more crop types and regions, and refining algorithms using real field data.
While challenges remain—such as ensuring predictive accuracy with limited data and integrating diverse inputs—the platform marks a significant step toward modern, sustainable farming.
The researchers have released code as open-source software. The implementation code with experiment scripts and results can be found at the GitHub repository. For further information, you can watch this Youtube video.
The research is published in Emerging Smart Agricultural Practices Using Artificial Intelligence (Wiley-IEEE Press, 2025). Read the full publication.
Murat Cardak passed his BSc with Honours and he is currently working as a Data Recovery Engineer at Ontrack.