Objectives and competences
The aim of this course is to introduce students to the application of modern technologies in the real-world context of smart agriculture, where they can apply their technical and agronomic knowledge to design, develop, integrate, and test sensor and robotic systems aimed at optimizing agricultural processes.
Content (Syllabus outline)
The AGRO-ROBOT course trains students in the use of modern technologies and interdisciplinary approaches to address current challenges in agriculture. Students will gain an understanding of basic agronomy, the role of sensors and robotics in monitoring and optimizing agricultural processes, and explore advanced technological solutions such as mechatronics, ROS (Robotic Operating System), computer vision, and artificial intelligence. As part of the practical work, students will work in teams to develop functional prototypes – from initial concept to working demonstration – using real-world data, sensors, and robotic platforms.
Learning and teaching methods
Lectures
Lab work
Projektno delo
Intended learning outcomes - knowledge and understanding
Upon completion of the course, the student will be able to:
• design and integrate a sensor-robotic system for tasks in smart agriculture, considering agronomic requirements, environmental conditions, and technological capabilities,
• work within an interdisciplinary development team composed of experts in agriculture, computer science, mechanical engineering, and electrical engineering to develop solutions for the digitalization of the agro-sector,
• use software tools (e.g., ROS) for planning, simulating, and managing mobile robotic platforms and sensors,
• design a data processing workflow for information captured by various sensors (e.g., spectral, visual, depth), and apply data fusion and artificial intelligence methods to support decision-making,
• apply basic agronomic knowledge (plants, soil, environmental influences) to adapt technical solutions to the real-world needs of agriculture,
• evaluate the impact of implementing new technologies on sustainable agriculture in terms of productivity, environmental protection, and social effects,
• lead and contribute to the execution of prototype development projects – from concept to functional demonstration – using a structured project approach,
• prepare and present technical and non-technical documents and project outcomes in both written and oral form, tailored to professional and public audiences
Transferable and key skills:
• Collaboration in interdisciplinary team, based on a team-oriented working approach.
• Use of information technologies for planning, simulation, and documentation of solutions.
• Independent project work focused on the development and testing of prototypes.
• Interdisciplinary understanding of the fundamentals of agriculture, computer science, mechanical and electrical engineering.
• Analytical thinking and solving complex problems with a technical-agronomic component.
• Critical evaluation of the impact of technological solutions on the environment, society, and productivity.
• Preparation and presentation of technical and popular content for different target audiences.
• Use of digital channels for project promotion and communication with relevant stakeholders.
Readings
Field Robot Event – pravila, ki jih vsako leto oblikujejo organizatorji tekmovanja. Desegljvo na https://fieldrobot.com
/ Field Robot Event – rules set by the organizers for a current year. Available at https://fieldrobot.com
Stafford, J. V. (Ed.). (2025). Precision agriculture '25 (2 vols.). Brill. https://doi.org/10.1163/9789004725232
RAKUN, Jurij, STAJNKO, Denis, RIHTER, Erik. Use of robotics in agriculture. V: Practical guide for the use of ICT in AET. [S. l.]: VIRAL, [2020]. ilustr. [COBISS.SI-ID 50824963]
VINDIŠ, Peter, LAKOTA, Miran, RAKUN, Jurij. Osnove preciznega kmetijstva : Skripta. 1. izd. Maribor: Univerza v Mariboru, Fakulteta za kmetijstvo in biosistemske vede, februar 2025. 1 spletni vir (1 datoteka PDF (87 str.)), ilustr. Digitalna knjižnica Univerze v Mariboru – DKUM. [COBISS.SI-ID 227280643]
Prerequisits
Basic knowledge of mathematics, computer science, and electrical engineering is recommended as well as English language.
The condition for selection is that the student consults with the project coordinator, defining his/her contribution to the project.
