Simulation Software in the Design and AI-Driven Automation of All-Terrain Farm Vehicles and Implements for Precision Agriculture

Precision agriculture depends on the automation and mechanization of agricultural equipment and vehicles in a variety of terrains, which increases productivity and sustainability. This review presents a comparative analysis of significant simulation software used in designing and developing automated agricultural systems, emphasizing their methodologies and significance in advancing farm technology. Artificial intelligence (AI) and machine learning (ML) methods are modeled, optimized, and integrated using key technologies such as MATLAB/Simulink, SolidWorks, ANSYS, AirSim, and Gazebo. The results demonstrate how these technologies improve agricultural automation's real-time decision-making, predictive maintenance, and system accuracy. Case studies illustrate their practical application in simulating all-terrain farm vehicles and specialized implements. The best tools for simulating autonomous navigation are AirSim and Gazebo, although MATLAB/Simulink is particularly adept at system-level AI modeling. This study takes a new approach to improving design, control, and environmental interactions by combining many modeling tools. This makes it easier to make agricultural automation systems that last longer and work better. It is suggested that future studies investigate the relationship between agricultural automation, AI, and simulation in greater detail to propel precision agriculture forward.