Autonomous navigation is a core enabler of smart agriculture, where path planning and trajectory tracking control play essential roles in achieving efficient and precise operations. Path planning determines operational efficiency and coverage completeness, while trajectory tracking directly affects task accuracy and system robustness. This paper presents a systematic review of agricultural robot navigation research published between 2020 and 2025, based on literature retrieved from major databases including Web of Science and EI Compendex (ultimately including 95 papers). Research advances in global planning (coverage and point-to-point), local planning (obstacle avoidance and replanning), multi-robot cooperative planning, and classical, advanced, and learning-based trajectory tracking control methods are comprehensively summarized. Particular attention is given to their application and limitations in typical agricultural scenarios such as open-fields, orchards, greenhouses, and hilly slopes. Despite notable progress, key challenges remain, including limited algorithm comparability, weak cross-scenario generalization, and insufficient long-term validation. To address these issues, a scenario-driven “scenario–constraint–performance” adaptive framework is proposed to systematically align navigation methods with environmental and operational conditions, providing practical guidance for developing scalable and engineering-ready agricultural robot navigation systems.

Stable and uniform pesticide application is essential for precision crop protection, input-use efficiency, and the reduction of off-target losses in large-scale farming systems. Large boom sprayers are important agricultural machines for high-efficiency crop protection, but their wide and flexible booms are highly sensitive to terrain-induced excitation, chassis motion, liquid sloshing, and hydraulic nonlinearities. These disturbances can cause roll, yaw, vertical oscillation, and boom-end height variation, thereby reducing spray uniformity, increasing drift risk, and threatening operational safety. Hybrid active–passive boom suspension systems have therefore become a key enabling technology for modern precision spraying. This review summarizes research progress in the structural design, dynamic modeling, sensing, and control of boom suspension systems for large-scale agricultural sprayers. Mainstream commercial machines commonly use double-pendulum active–passive suspension architectures, in which passive components attenuate high-frequency vibration and active subsystems improve low-frequency terrain-following performance. Recent studies have advanced electro-hydraulic actuation, disturbance compensation, adaptive control, multi-sensor fusion, and field evaluation methods; however, a unified framework for coupling boom dynamics, spray quality, sensing accuracy, and whole-machine operation remains incomplete. Key challenges include rigid–flexible–hydraulic coupling, underactuated control, uncertain parameters, external disturbances, and posture estimation errors caused by boom elastic deformation and sensor noise. Future research should emphasize rigid–flexible–fluid-coupled modeling, adaptive output-feedback control with disturbance and resonance suppression, terrain-preview and multi-source perception, and coordinated chassis–boom-spray control. These developments can support more stable, efficient, and environmentally responsible spraying operations in modern precision agriculture.