Simulation and Field Testing of Autonomous Agricultural Robots for Enhanced Crop Monitoring and Field Management

Abstract

The increasing scale and complexity of modern agricultural operations have intensified the need for intelligent automation to address labor shortages, operational inefficiencies, and the demand for real-time crop intelligence. Autonomous agricultural robots represent a transformative solution capable of enhancing crop monitoring, precision field management, and decision-making accuracy. This study presents a comprehensive investigation into the design, simulation, and field testing of an autonomous ground-based agricultural robot developed for crop surveillance and field operations. The system integrates multi-sensor perception, autonomous navigation, and adaptive control algorithms to perform real-time crop monitoring under dynamic field conditions. Simulation-based validation was conducted using robotic modeling environments to evaluate navigation accuracy, obstacle avoidance, and energy efficiency prior to physical deployment. Field experiments were then carried out across diverse crop fields to assess operational robustness, data acquisition accuracy, and system reliability. Performance metrics including path deviation, detection accuracy, operational efficiency, and environmental adaptability were analyzed in detail. The results demonstrate that autonomous agricultural robots can significantly enhance field monitoring efficiency while reducing human intervention and operational costs. The study confirms the viability of robotic automation as a cornerstone technology for sustainable and precision-driven agriculture.