This study experimentally investigates the breakup mechanisms and atomization characteristics of liquid jets in subsonic crossflows and develops a penetration depth model that incorporates the incidence angle. Experimental data show that the model fits well, with a minimum R² value of 0.924 and an average of 0.969. High-speed imaging techniques were used to systematically analyze the effects of liquid- and gas-phase Weber numbers and incidence angles on the penetration and atomization of liquid jets. The experimental results indicate the following: (1) As the liquid Weber number (We_l) increases, the penetration depth increases, while the gas Weber number (We_a) is inversely related to penetration depth. (2) A decrease in the incidence angle (ranging from 45° to 90°) significantly reduces penetration performance. (3) As We_a increases, the volume median diameter (VMD) of droplets decreases by 61.70% to 83.09%, while smaller incidence angles cause a 42.96% increase in the VMD. The VMD shows a non-linear trend with respect to We_l, reflecting the complex interaction between inertial forces and surface tension. These findings provide a theoretical basis for understanding the atomization behavior of transverse jets and the key parameters affecting penetration and droplet formation. The results are of practical significance for the structural optimization and performance enhancement of air-assisted atomizing nozzles used in precision agricultural spraying systems.

Crop row identification and navigation line extraction are essential components for enabling autonomous operations of agricultural machinery. Aiming at the soybean–maize strip intercropping system, this study proposes a LiDAR-based algorithm for crop row detection and navigation line extraction. The proposed method consists of four primary stages: point cloud preprocessing, crop row region identification, feature point clustering, and navigation line extraction. Specifically, a combination of K-means and Euclidean clustering algorithms is employed to extract feature points representing crop rows. The central lines of the crop rows are then fitted using the least squares method, and a stable navigation path is constructed based on angle bisector principles. Field experiments were conducted under three representative scenarios: broken rows with missing plants, low occlusion, and high occlusion. The results demonstrate that the proposed method exhibits strong adaptability and robustness across various environments, achieving over 80% accuracy in navigation line extraction, with up to 90% in low-occlusion settings. The average navigation angle was controlled within 0.28°, with the minimum reaching 0.17°, and the average processing time remained below 75.62 ms. Moreover, lateral deviation tests confirmed the method’s high precision and consistency in path tracking, validating its feasibility and practicality for application in strip intercropping systems.

Pesticide application efficiency is fundamentally governed by the spatial distribution of droplet deposition. However, characterizing this dynamic process is challenging due to complex environmental and canopy variables. Consequently, conventional offline sampling methods lack the temporal and spatial resolution required for modern intelligent spraying systems. This review systematically examines recent progress in droplet deposition detection. We first revisit traditional methods like water-sensitive paper, addressing high-coverage quantification biases and fluorescence-based techniques. Next, we analyze real-time sensing technologies, including capacitive and optical sensors, highlighting their responsiveness and inherent physical constraints. Furthermore, deep learning approaches for droplet detection, overlap segmentation, and geometric-to-physical regression are discussed. While these methods substantially enhance feature extraction, they often struggle with cross-scenario generalization. Ultimately, current techniques face inherent trade-offs among real-time capability, quantification accuracy, and environmental adaptability, remaining insufficient for complex field conditions. To enable reliable closed-loop control in precision plant protection, future research must prioritize multi-modal sensor fusion, the integration of data-driven and physics-based models, and real-time deployment via edge computing.

This review comprehensively summarizes the recent research progress of multi-source sensing technologies and intelligent modeling methods in vegetation monitoring. It elucidates the physical mechanisms and complementary natures of core active and passive technologies, including optical hyperspectral remote sensing, LiDAR structural detection, and microwave/millimeter-wave radar. Furthermore, it systematically analyzes advanced data processing methods, covering physics-based radiative transfer models, data-driven machine learning, Physics-Data Dual-Driven Modeling transfer learning, and multi-source data fusion strategies. The paper highlights successful applications across diverse typical scenarios, such as crop precision nitrogen diagnosis, forest biomass estimation, crop lodging risk prediction, and vegetation stress assessment. Finally, it discusses critical challenges like data heterogeneity and model generalization, while presenting a future outlook focused on building collaborative “space-air-ground” integrated monitoring networks and advanced AI fusion methods.

Accurate canopy thickness measurement is vital for advancing smart orchard sprayers and reducing pesticide use. We present three FMCW radar-based algorithms to estimate canopy thickness and assess their accuracy through experiments. Laboratory tests on simulated canopies showed a strong correlation between estimated and actual thickness. However, stability and relative error were significantly affected by leaf area density (LAD) and detection distance. When distance was 40.0–110.0 cm, LAD 1.5–4.2 m²/m³, and actual thickness 30.0–120.0 cm, the coefficient of variation and relative error remained within ±14%. Preliminary outdoor trials on three isolated tree canopies served strictly as an early proof-of-concept, not a full orchard evaluation. The Db algorithm performed poorly, with relative error up to 74.9%, whereas the Da and Dc methods proved robust, with maximum relative errors of 11.93% and −15.19%, respectively. These findings highlight that, with further refinement, Da and Dc algorithms are highly promising for precision variable-rate spraying systems in orchards.

Air-assist atomization exploits high-velocity gas streams to shear liquid phases, achieving fine droplets at supply pressures significantly lower than those required by conventional pressure atomization. This technology is extensively deployed across diverse sectors, including pesticide spraying, food spray drying, sanitization/disinfection, and combustion atomization. Although specific operational fields dictate vastly contrasting droplet size distributions, velocities, and deposition uniformities—with target diameters spanning from 200 μm—their foundational atomization mechanisms remain inherently unified, governed primarily by toroidal vortex-induced primary breakup and Kelvin–Helmholtz/Rayleigh–Taylor (KH–RT) or Taylor Analogy Breakup (TAB) secondary breakup. This review systematically parses the atomization mechanisms, critical performance metrics, numerical simulation frameworks, and experimental characterization methodologies of air-assist nozzles. Crucially, from a novel “cross-disciplinary custom design” perspective, we contrast the optimal droplet parameter windows across the agricultural, food, sanitization, and combustion sectors (e.g., electrostatic plant protection yields a 203–1350% increase in abaxial leaf deposition; mine wind-assisted misting achieves a >90% collection efficiency for PM₁₀ dust; and SCR air-assisted injectors reduce the Sauter Mean Diameter (SMD) to 25 μm). Synthesized insights reveal that segmented VOF-to-DPM transition frameworks, corner-vortex-induced breakup theories, and closed-loop adaptive control architectures possess substantial cross-domain migration value. Current bottleneck challenges are highlighted, including the mesh dependency of droplet collision–coalescence models in dense spray regimes, the absence of robust atomization constitutive formulations for non-Newtonian fluids, and the ongoing paradigm shift from open-loop presetting to intelligent closed-loop regulation. This work establishes a comprehensive theoretical foundation and technical roadmap for cross-disciplinary integration and next-generation smart nozzle design in air-assist atomization.