12 May 2025
Anonymous Writer
The Master’s Program in Agrotechnology at Universitas Sumatera Utara held a public lecture on Friday, May 9, 2025, at the D.H. Penny Hall, Faculty of Agriculture, Universitas Sumatera Utara.
The lecture carried the theme “Reimagining the Application of AI in Soil Science.” The speaker was Prof. Budiman Minasny, Ph.D., a distinguished professor from the Sydney Institute of Agriculture, School of Life and Environmental Sciences, The University of Sydney.
The event began with an opening speech by the Head of the Master’s Program in Agrotechnology, Prof. Luthfi Aziz Mahmud Siregar, S.P., M.Sc., Ph.D., and was moderated by Dr. Ir. Mukhlis, M.Si., a lecturer in the Agrotechnology program. The lecture was attended by program secretaries, faculty members, and students from both the undergraduate and postgraduate Agrotechnology programs, as well as from the Doctoral Program in Agricultural Science.
In his opening remarks, Prof. Luthfi stated that the presence of Prof. Budiman Minasny, Ph.D. was intended to provide updates on the latest scientific developments, which could offer significant benefits and serve as a meaningful contribution to broadening the participants’ knowledge and understanding in science and technology.
In his presentation, Prof. Budiman explained that AI in soil science refers to intelligent systems capable of making decisions automatically without human intervention.
“AI encompasses a variety of techniques that utilize computers to solve diverse problems. One of the main components of AI is machine learning. Machine learning helps automate data processing, identify patterns within the data, and can then be used to build models for predictive applications,” Prof. Budiman explained.
Through this public lecture, Prof. Budiman emphasized that although AI has become highly advanced and capable of many functions, a strong scientific foundation is still essential. Relying solely on data is not sufficient without a clear understanding of underlying principles. It is necessary to apply scientific methods and frameworks to ensure that AI models serve as valid tools for knowledge generation.
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