From Fields to Algorithms: Prof. Dr. Aleksandar Sedlar talks about the Importance of Farmer's Feedback in Precision Agriculture
To achieve the aim of advancing precision agriculture by developing a platform that ensures data gathered through precision farming is well-suited for AI tasks, STELAR turns to experts for valuable insights, ensuring that the platform is both suitable for its purpose and capable of integrating data that is often scattered in different places and forms.
Precision agriculture, a transformative approach to farming, leverages advanced technologies and data analytics to optimise crop production, improve efficiency, and enhance sustainability. As the global population continues to grow, the demand for food rises, pushing the agricultural sector to innovate and adapt.
Prof. Dr. Aleksandar Sedlar, an expert in this field and the head of the Master’s Program at the Faculty of Agriculture, University of Novi Sad, brings valuable expertise to this field.
In this interview, we will delve into the insights of Prof. Dr. Sedlar as he discusses the primary benefits of precision agriculture for farmers, the challenges they face in adopting these techniques, and the role of IT and AI in transforming agricultural practices.
The Professional Path of Prof. Dr. Aleksandar Sedlar
Can you tell us about your academic and professional background? To be more exact, what led you to specialise in precision agriculture?
I am a full professor and chief of the Master’s program in Precision Farming. My primary areas of focus revolve around implementing crop monitoring and VRA maps in agricultural practice.
What do you see as the primary benefits of precision agriculture for farmers?
The primary benefits of precision agriculture lie in its diverse approaches to crop production. These approaches enable agricultural producers to analyse their production using historical data, including information on yield, vegetation index, soil characteristics, and weather conditions. This wealth of data allows for informed decision-making regarding the technology and agricultural techniques needed for the upcoming season.
"AI is expected to play a key role in processing and utilising this data in the future."
What are the biggest challenges faced by farmers when it comes to adopting precision agriculture techniques?
The significant challenges include educating agricultural producers and providing them with suitable experts who can offer advice and guidance on adopting precision agriculture techniques in their productions. Advice should come from individuals with diverse backgrounds. At first from experts with fundamental agricultural knowledge. IT, electrical and mechanical engineering can help as technical support but they are not suitable experts who can offer advice and guidance on adopting precision agriculture.
How can precision agriculture contribute to addressing the challenges of food security and sustainability, given the increasing global population?
Precision agriculture aims to decrease production costs, thereby relieving agricultural producers and improving production suitability.
Within STELAR, we are developing tools that will help produce prescription maps for users. In what ways does the implementation of prescription maps for variable rate treatments or precision irrigation help the future of sustainable agriculture?
In our market and globally, there are numerous softwares for producing prescription maps. However, in most cases it is not good software because IT experts make them without inputs from real agricultural production. Developing effective software for implementing variable rate treatment typically requires years of refinement. Such software, when properly developed, can significantly optimise agricultural production.
What types of data do you believe are most crucial for precision agriculture and how can AI help in processing and utilising this data?
The most crucial data include information on vegetation index, soil characteristics, and weather conditions. AI is expected to play a key role in processing and utilising this data in the future.
How applicable is precision agriculture in Serbia’s agricultural landscape? Can you share any success stories or case studies where precision agriculture has significantly improved crop yields or efficiency in Serbia?
In Serbia, precision agriculture is applicable, but it is still early to establish a representative case study.
In conclusion
While precision agriculture holds great promise, it is not without challenges. Despite these challenges, the integration of IT and AI in precision agriculture is indispensable. AI has the potential to process vast amounts of crucial data, and transform this information into actionable insights.
However, it is essential that these AI tools are developed with substantial input from agricultural experts to ensure their effectiveness. With a collaborative approach, combining the expertise of both IT professionals and agricultural scientists, precision agriculture can truly revolutionise the farming landscape.
STELAR’s tools and platform embody this collaborative spirit by actively soliciting feedback from farmers. This iterative process, involving continuous feedback loops from farmers and agricultural experts, will enhance the usability and relevance of the tools, ultimately improving their impact on agricultural productivity and sustainability.
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