Panagiotis Karagiannis on Designing Flexible Tools for Smart Farming
In this interview, we speak with Panagiotis Karagiannis, Project Manager at the Laboratory for Manufacturing Systems & Automation (LMS) at the University of Patras. As an important contributor to the Robs4Crops project, Panagiotis shares his insights into how robotics, automation, and adaptable platforms such as the Farming Controller are helping bridge the gap between digital innovation and real-world agricultural practice.
With a background in control systems and manufacturing, he brings a practical, research-driven perspective to the challenges and opportunities in smart farming.
Exploring Our Guest’s Motivation and Background in Smart Farming
Could you tell us about your professional journey so far and what led you to work in the field of intelligent farming solutions?
I got my diploma in electrical and computer engineering at the University of Patras, and my early career involved working on robotics and automation for manufacturing applications in the Laboratory for Manufacturing Systems and Automation (LMS) at the University of Patras, where I started as a Research Engineer.
However, having experience in intelligent control systems in the manufacturing domain, we wanted to explore similar applications in other sectors, identifying agriculture as an area facing considerable challenges related to productivity, sustainability, and labour shortages.
Thus, my transition into intelligent farming solutions emerged from the realisation that smart technologies, originally developed for manufacturing, could profoundly support agriculture in becoming more efficient, sustainable, and user-friendly.
What personally motivates you to explore smart systems and automation in agriculture, and what keeps your interest in this area alive?
The primary motivation is that it is a new field we are investigating as a lab, so there is a lot of room to explore technologies and ideas we have already successfully developed and tested in the manufacturing industry.
Another motivation is that we can see the direct impact such solutions have on agriculture. We have the opportunity to test our technologies immediately in the field and see results within the same season, which is much easier and simpler compared to industrial testbeds and applications.
Lastly, the multidisciplinary nature of smart agriculture – combining mechanical engineering, robotics, data analytics, and agronomy – keeps the field continually fresh and engaging.
Bringing Smart Control to Farming’s Future
Through your current role at LMS, what are some of the key projects or responsibilities you are focusing on at the moment?
Currently, I have the role of Project Manager in LMS, leading and coordinating multidisciplinary projects like Robs4Crops. My key responsibilities include overseeing the development of adaptable control platforms such as the Farming Controller, ensuring user-oriented design, and driving collaborative research across industry and academia.
Additionally, I am coordinating a project focused on XR technologies for robotics training in industry, which could also become relevant in agriculture as another niche application area.
You have developed adaptable systems, such as the Farming Controller in Robs4Crops, which bring together autonomous machinery and smart implements. In your view, what are the biggest challenges and opportunities when building such flexible, user-oriented platforms?
The key challenge when developing such complex systems is the interoperability of the subsystems. On our side, we use ROS/ROS2 in many robotic applications as the main communication channel, which is not commonly used in agriculture.
In Robs4Crops, the team also worked on a specific version of ISOBUS, a new CAN-based communication protocol that was still being standardised at the time and not yet adopted by machinery providers. Not having a common, standardised communication channel known by all partners posed a significant challenge in defining and agreeing on the architecture and data model for the Farming Controller.
Another challenge was the creation of intuitive interfaces that meet the diverse needs of farmers, offering different functionalities in the PC version (e.g. defining farms, machinery, farming operations) and in the mobile version (e.g. tracking the status of machinery and operations via smartphone).
On the other side, we viewed these challenges as opportunities to improve efficiency, minimise costs for farmers, and empower them with greater autonomy through user-oriented platforms. These tools can also support environmental sustainability by optimising resource use, minimising waste, and enabling precision agriculture practices.
Connecting Robotics and Farming Data
Based on your experience, how do you see your work connecting with the ambitions of European projects like STELAR, which aim to make agricultural data more accessible, better linked, and easier to find and use?
One of the main functionalities of the Farming Controller was data capture from all the sensors available in the field, and storing this data for future use. High-quality data is a challenge across domains, so we see a direct link between our developments and the goals of STELAR, especially in terms of improving data accessibility and usability.
Robs4Crops generated real-time agricultural data, and our focus in the Farming Controller was on structuring it properly, ensuring interoperability, and supporting open access and integration across platforms, all of which align with STELAR’s objectives.
Many users across the agrifood sector struggle to work with data that is scattered, inconsistent, or poorly searchable. From your perspective, what practical steps could help bridge this gap and make data genuinely useful for farmers and other end-users?
Firstly, as described above, we need a consistent data model to store good-quality real-time data from the field. Secondly, the standardisation and definition of protocols are essential for effective data management and eventually extracting meaningful information.
Finally, farmers must understand the value of using data effectively. They need training and support to understand the importance of data and how it can benefit their work. Creating clear standards and ensuring transparency through user-centred design can significantly improve data accessibility, usability, and searchability across the agrifood sector.
Next-Gen Farming: Control, Data, and Efficiency
Looking ahead, how do you think smart farming solutions and data-driven tools will evolve over the next decade, particularly in terms of meeting the everyday needs of farmers?
Over the next decade, I believe AI will play a major role in agriculture. Autonomous systems, smart farming solutions, and other AI-based technologies, such as machine learning, will support features like predictive analytics, proactive decision-making, and real-time adjustments in the field. Cloud-based services, similar to the Farming Controller, will become more widespread, enabling seamless data integration and remote monitoring.
Beyond the technical aspects, a key issue that must be addressed is the need to educate farmers on the benefits of these tools so that they are open to adopting them and willing to invest in them.
Conclusion
Panagiotis Karagiannis offers a valuable perspective on the growing role of smart control platforms in agriculture. Through tools like the Farming Controller developed within the Robs4Crops project and his ongoing work at LMS, he highlights the importance of data accessibility, platform flexibility, and user involvement in the journey toward next-generation farming.
Find additional resources on smart farming at the STELAR blog and connect with us on LinkedIn.
