This study highlights the necessity and potential of integrating artificial intelligence (AI) into agricultural higher education, with a specific focus on the University of Ruse. A comprehensive literature review confirms AI's transformative influence on both educational practices and agricultural production systems. The proposed Master of Science program in Artificial Intelligence in Agriculture aims to bridge the gap between traditional agricultural curricula and contemporary industry needs by developing competencies in machine learning, computer vision, predictive analytics, and agricultural data management. The University of Ruse possesses the institutional infrastructure and interdisciplinary expertise required to support such an initiative, including specialized modules and research facilities. However, effective implementation depends on addressing challenges related to faculty qualification, digital infrastructure, and stakeholder collaboration, particularly with farmers and agribusiness partners. The research contributes to the broader discourse on digital transformation in higher agricultural education and outlines a scalable model for developing AI-driven competencies. The outcomes are expected to strengthen employability, innovation capacity, and the transition toward data-informed, sustainable agricultural practices.

The study addresses the challenge of accurately simulating and visualising the kinematics of agricultural machinery during field operations. The research is motivated by the current lack of comprehensive guidelines for selecting optimal movement and turning modes under varying forward speeds, working widths, and field geometries. A spreadsheet-based environment was utilised to perform simultaneous kinematic simulation and trajectory visualisation. Turning manoeuvres were modelled using smooth composite curves, consisting of straight segments, clothoids, and circular arcs, with trajectories represented in a Cartesian coordinate system through geometric transformations including translation, rotation, and mirror symmetry. Continuity between curve elements was ensured by dimensional chains linking abscissas, ordinates, and direction angles at their start and end points. The influence of key operational factors—forward speed, angular turning velocity, working direction, and field boundaries—was evaluated for a range of turn types, including semicircle, pear-shaped, figure-eight, side exit, U-turn, and P-turn manoeuvres. Field experiments conducted on selected patterns confirmed that the proposed approach can reproduce actual trajectories with sufficient practical accuracy. These results demonstrate that spreadsheet-based kinematic modelling is a robust and accessible tool for optimising tractor–implement movement, enhancing operational planning, and providing a reliable framework for further research into machinery performance under complex field conditions.