Preformed porous media (PPM) technology has emerged as a transformative approach to enhance heat and mass transfer in vacuum freeze-drying (VFD) of agricultural and food products. This review systematically analyzes recent advances in PPM research, with particular focus on spray freeze-drying (SFD) as the dominant technique for precision pore architecture control. Empirical studies confirm PPM’s efficacy: drying time reductions of 20–50% versus conventional VFD while improving product quality (e.g., 15% higher ginsenoside retention in ginseng, 90% enzyme activity preservation). Key innovations include gradient porous structures and multi-technology coupling strategies that fundamentally alter transfer mechanisms through: resistance mitigation via interconnected macropores (50–500 μm, 40–90% porosity), pseudo-convection effects enabling 30% faster vapor removal, and radiation enhancement boosting absorption by 40–60% and penetration depth 2–3 times. While inherent VFD limitations (e.g., low thermal conductivity) persist, we identify PPM-specific bottlenecks: precision regulation of pore structures (<5% size deviation), scalable fabrication of gradient architectures, synergy mechanisms in multi-field coupling (e.g., microwave-PPM interactions). The most promising advancements include 3D-printed gradient pores for customized transfer paths, intelligent monitoring-feedback systems, and multiscale modeling bridging pore-scale physics to macroscale kinetics. This review provides both a critical assessment of current progress and a forward-looking perspective to guide future research and industrial adoption of PPM-enhanced VFD.

As a core component of agricultural machinery autonomous navigation, path tracking control holds significant research value. The pure pursuit algorithm has become a prevalent method for agricultural vehicle navigation due to its effectiveness at low speeds, yet its performance critically depends on the selection of the look-ahead distance. The conventional approaches require extensive parameter tuning due to the complex influencing factors, while fixed look-ahead distances struggle to balance the tracking accuracy and adaptability. Considerable effort is required to fine-tune the system to achieve optimal performance, which directly affects the accuracy of the path tracking and the results in the cumbersome task of selecting an appropriate goal point for the tracking path. To address these challenges, this paper introduces a pure pursuit algorithm for high-clearance sprayers in agricultural machinery, utilizing a self-adjusting look-ahead distance. By developing a kinematic model of the pure pursuit algorithm for agricultural machinery, an evaluation function is then employed to estimate the pose of the machinery and identify the corresponding optimal look-ahead distance within the designated area. This is done based on the principle of minimizing the overall error, enabling the dynamic and adaptive optimization of the look-ahead distance within the pure pursuit algorithm. Finally, this algorithm was verified in simulations and bumpy field tests under various different conditions, with the average value of the lateral error reduced by more than 0.06 m and the tuning steps also significantly reduced compared to the fixed look-ahead distance in field tests. The tracking accuracy has been improved and the applicability of the algorithm for rapid deployment has been enhanced.