The agricultural sector is undergoing a digital transformation due to modern automation, robotics, sensing, and simulation technologies. This research explores the use of digital human models (DHMs) in the Gazebo virtual environment to enhance agricultural workflows, improve human–robot interaction, and ensure safety. We propose a framework that models typical agricultural scenarios, such as field mapping, harvesting efficiency control, crop inspection, obstacle avoidance, and theft detection. DHMs represent farm workers interacting with mobile autonomous systems, stationary sensors and sensor networks. The DHMs are equipped with generic and task-specific animations; the latter include such activities as crop harvesting and field inspection. The simulation environment features agricultural settings with dynamic obstacles and predefined work zones. Performance in each scenario is proposed to be evaluated using metrics such as a task’s completion time and obstacle avoidance rate. Results of preliminary simulation of the proposed simplified scenarios in the Gazebo simulator demonstrated a high potential of DHMs and Gazebo to optimize agricultural workflows and improve human–robot interaction. This study provides a foundation for leveraging simulation technologies to address practical challenges in agriculture and to support the design and validation of intelligent agricultural systems.