Tomaž Bokan for STELAR: Real Stories of Tech-Driven Farming
The way we farm is changing. With pressures like climate uncertainty, labour shortages and the need to produce more with fewer resources, agriculture must adapt. But what does that look like in practice?
This time, we hear from Tomaž Bokan, Senior Project Manager at the Innovation Technology Cluster Murska Sobota (ITC) and Coordinator of Slovenia’s Digital Innovation Hub for Agrifood. With a background in broadcast technology and a shift into digital agriculture, he brings a practical perspective on how tools like AI, IoT and satellite data are already helping farmers work more efficiently.
From predicting frost risks to optimising fertiliser use, EU-funded projects like GREEN.DAT.AI, DIVINE, Farmtopia and STELAR are testing real-world applications of these technologies. The goal? To make data-driven farming accessible – not just for large agribusinesses, but for smaller farms across Europe.
So, how does it work? Where are the biggest challenges? And what comes next? Let’s break it down.
Exploring Our Guest's Motivation and Background
You have a rich background in broadcast media, digital TV, and interactive technologies. Can you share a bit about your career journey and how you transitioned into digital agriculture?
My background is primarily technical, and this focus and passion were always somehow directed towards technology and its usability. After my studies, I ended up in a company building broadcasting and television equipment, which was operating in an international market. Actually, that was a very rich experience.
Over time, I somehow also ended up in a privately owned company where I was a co-owner. I was working a lot abroad on different projects in the broadcasting sector, meaning digital television and related technologies. After 15 years working more or less away from home, basically all over the world, and also with the changes in the markets and the evolution of technology, it was time to change or to do something a little bit different.
In 2017, I transitioned to work more on technology-driven projects, which are co-financed by the European Commission and other bodies, primarily on new technologies in robotics. Then, quickly, in a few months, I actually also started to work in the agri-food sector. This sector, when I compare it with my previous work and experience, is, at least in its major parts, not so well developed. There is a lot of potential and many new things that can still happen, and we can further develop our environment as well. This is very interesting from my perspective.
As both the DIH Agrifood Coordinator and a Senior Project Manager at Innovation Technology Cluster (ITC), you are working at the intersection of technology, agriculture, and innovation. How do these roles align?
Digital Innovation Hub for Agrifood is a non-formal collaborative framework. It is not an organisation as such; it involves different stakeholders and actors. Everybody can somehow find their place in this story.
Together, we are trying to develop projects or actual actions in the field, which then support the building of this ecosystem that supports all the actors. Actually, it is not just farmers; it is also some industry players who would like to bring solutions to the market, or farming advisory services, or universities that would like to develop something close to real use cases.
At the Innovation Technology Cluster Murska Sobota, which is an organisation, we are managing this Digital Innovation Hub Agri-Food, but we are just one of the organisations involved. As mentioned, we are a private, non-profit research organisation.
In this organisation, my work is mainly focused on technology projects related to technological innovations, piloting in our environment, bringing together different actors, showing them best practices, connecting them, and building relationships. That is a very good connection, and these roles align very well.
Practical Applications and Impact
Your work involves integrating IoT, precision farming, and Earth observation. Can you share an example where these technologies have significantly improved agricultural decision-making?
These digital technologies, along with other advancements, are making a significant contribution. For example, if we consider arable farming, which has its own specifics, we can now apply fertilisers and pesticides based on satellite images, using highly precise machinery. This allows us to avoid applying treatments in places where they are not needed, or to adjust the quantities and carry out spraying exactly where it is required. This has very positive effects on the environment, but also on the efficient use of resources, time, and energy.
Another example is pest and disease detection, where we can analyse what is happening in the fields, and farmers can be alerted that there is a specific area they need to inspect or check because something may be occurring. All of these approaches I have mentioned would not be possible without the technology.
In other types of production, for example, vineyards or orchards, we now have various decision support systems that use data from local weather stations, IoT sensors in the ground measuring humidity, or pheromone traps that track insect concentrations. These systems provide advice on when to irrigate, when to apply certain treatments such as pesticides, and they also support frost protection, which is a significant issue in our region.
For instance, during a time when daytime temperatures may already reach 17 or 18 degrees, we can still experience two days of frost with temperatures dropping to minus three or four degrees. This can place the entire production at risk. Frost protection systems can be activated, spraying water onto the plants to prevent damage from the frost.
At the end of the process, consumers also benefit from all of this technology. We now have traceability systems that can show exactly how a product was grown on a particular parcel of land, how it was processed, and how it was delivered to the store. There are many solutions we can develop and that we already have, thanks to these new technologies that are now available.
What are the biggest challenges you see in implementing these technologies across different regions in Europe?
The sector varies greatly from country to country. The structure of farms differs significantly across Europe. For example, what is considered a large farm in Hungary or Romania is not the same size as a large farm in Slovenia. That is simply a fact.
On the other hand, the majority of European farms are family-run, with around two-thirds being smaller than five hectares. Many of these smaller farms are not as well organised or as well equipped as larger farms. Larger farms typically employ staff such as agronomists, they have modern machinery, and they tend to keep up with the latest advancements, including the use of digital systems, as they must remain competitive in the market.
The real challenge lies with the smaller farms, where we often face issues such as an ageing farming population, limited digital skills, poor connectivity, and financial constraints. These are some of the biggest barriers we need to address to support the sector’s future.
Not every farmer can, or would like to, become a large-scale operation. However, even smaller producers should be able to work efficiently, in a way that is environmentally responsible and avoids wasting resources. This is exactly where much of our work is focused – helping to preserve and strengthen this part of the sector.
Across many of our projects, we aim to connect different stakeholders – farmers, policy makers, industry players – and demonstrate the benefits of digital and technological solutions. It is particularly important to show smaller farms that these solutions can bring them real, tangible value.
Insights from EU-Funded Projects
You have been involved in multiple EU-funded projects like GREEN.DAT.AI, DIVINE, and Farmtopia. Could you highlight a key takeaway from these projects in terms of how AI and digitalisation are shaping the future of farming?
There are different goals and initiatives that evolve over time. Since we are consistently working in the agri-food sector, I can share a few examples from our recent projects.
In the GREEN.DAT.AI project, for instance, we are running a pilot in Slovenia focused on arable farming. We are using digital twin technology to improve farmers’ decision-making processes. The University of Maribor has developed AI models based on historical fertilisation maps, farm data, satellite images, and weather conditions. These models generate updated maps for the current or upcoming seasons, significantly reducing the effort required from farmers to prepare this information from scratch, especially when managing many parcels. This approach saves both time and resources.
In addition, this digital twin model is also used to implement an alert system. It detects certain zones on the parcel where there may be a deviation, and then the farmer can be alerted – “Okay, take a look, something is happening,” – because we see a deviation on the image.
At the end, the final result of this is that we are preparing some harvest scenario sequences. When the harvest sequence starts, it is important to begin in the right order – to start with the most mature fields and follow with the less mature ones later on. From a human perspective, it is difficult to survey so many fields and to set such priorities without assistance.
Regarding the DIVINE project which you mentioned, it is targeting a slightly different story. Here, we are dealing with farm production and the economics of data, mainly to support dairy production. We have a data space-based platform, and farming advisors are working together with farmers to optimise their business plans for the future, or to identify areas where they deviate from the average or from surrounding farmers.
And finally, Farmtopia, which again is a bit different, is more focused on small and medium-sized farms. We aim to digitalise the way farmers keep records on their farm – how they use this data for reporting to authorities or for Common Agricultural Policy (CAP) measures. In this project, we are testing a fieldbook tool with farmers. These are three examples of slightly different topics being addressed – but all within the agri-food sector.
The STELAR project is working on a Knowledge Lake Management System (KLMS) to facilitate smart agriculture and enhance food safety applications. How do you see the approach of before mentioned projects aligning with STELAR’s objectives?
I do not know enough about STELAR for sure to be able to answer this clearly. But what is certain, and what we all face, is that we need to have proper data. This data needs to be collected and interpreted in the correct way to enable future decision-making. I think that is the starting point.
Actually, we already have farmers, governments, and other stakeholders collecting a significant amount of data, perhaps, but how this data is structured, how it is described, what this data actually represents, and perhaps also the quality of the data collection – these aspects are somewhat questionable in some cases. We need to work on the data itself, and this data should then be used, as I understand is being done by the STELAR project, to turn what I would call data lakes – huge amounts of data – into knowledge lakes, to actually generate knowledge from that.
Because only this knowledge can then support valuable insights – to predict specific outcomes, to support management decisions, to inform policy-making measures, and to guide operational improvements. To achieve this, we also need to work on data exchange.
The topic of European data spaces is very relevant at the moment, and it is also the right direction, because we need to have data ownership and control, as outlined by these rules and guidelines. That should be the basis for all future work on this data – to use it effectively in AI applications, ensure interoperability, and enable broader data-driven solutions.
It is very well aligned with what we are doing in other projects, and I believe there will be even more similar projects in the future, because it is not easy to solve this challenge in a short period or with just a few initiatives.
Future Outlook and Farmer Readiness
Looking ahead, what do you think will be the next big breakthrough in AI and data-driven agriculture?
That is kind of a million-euro question, which I would be very happy if I could answer. But I think the pace of these technological changes makes it hard to predict what exactly will happen. In any case, there will be some incremental advancements in several areas. I think that is more likely to happen than some major breakthrough in one specific area. But for sure, the market will grow. We have all the studies indicating this, and we also see that the more development is happening, the more usability is there, and this means that it will be consumed more.
On the other hand, it is even expected that we will need to use these technologies. According to some studies from the European Union, in their outlooks, they are actually saying that we need to compensate for all the climate changes and other challenges that are happening by using this technology and new approaches. Otherwise, we could face challenges in the food production systems in the future.
I would imagine that, for example, some AI advisory services, which we have now, could evolve to actually provide guidance to farmers, maybe through chatbots or camera-based systems. This would bring all this knowledge and the required skills closer to them to solve problems on the spot, without waiting for an advisory service to tell them what to do in the field. This will be automated in a way.
Also, this decision support will develop because we have all the data. We have satellite images, we have weather trends, we have market conditions, we have labour shortages. All this data is very useful if you can work with it and use it to optimise production, to be able to predict trends, to know what you should produce, what food is needed, based on the climate, and what is most suitable for the parcels you have.
Then, robotics and automation, which are already happening, will become even more important due to labour shortages. All of these systems are also supported by data, including the operation of greenhouses – when they should irrigate, when they should open or close the ventilation. All of these aspects will be very much affected by the technology.
How can farmers and agribusinesses better prepare for the shift towards digital agriculture, and what role do digital innovation hubs like DIH Agrifood play in this transition?
Everything will move more towards digitalisation, and we need to face this now. We need to understand how the technology can help us, what skills we need to be able to use the technology, and also – this is maybe on the level of the farmers and producers – but on the other hand, we also need governments, regulators, and all these bodies to understand how the sector works. They need to understand how the data, if it is collected, can be used for some greater good, not just for reporting or for controlling purposes.
All of these actors need to be skilled and should be presented with all of these advancements. For example, if we have specific types of data, we can predict outcomes, solve problems, or develop effective strategies. The basis for this is to start collecting data or storing it in a proper way, and to ensure that we have useful systems. We should not collect data just for bookkeeping or limited administrative purposes. There should be tools that can use this data in a very efficient way, taking into account far more information than one person can process or decide upon.
Everybody in this chain – all these actors – needs to understand this because they are all important. Everybody has to play their role in this. I believe this needs to happen very quickly, or we need to start with it now.
Conclusion
As we have heard today, the digital transformation of agriculture is no longer a distant goal but a pressing reality. With the right tools, skills, and collaborative frameworks, AI and data-driven technologies can empower all actors in the agrifood sector to make smarter, faster, and more sustainable decisions. The time to act is now, and it starts with understanding the value of data and working together to harness its full potential.
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