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.

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.

Agricultural spraying has evolved from conventional passive drift mitigation toward active, intelligent precision management. This review synthesizes advancements in agricultural precision spraying, evaluating both established passive drift-reduction techniques and emerging active application systems. The analysis emphasizes three core technological pillars: multi-sensor target perception, real-time variable-rate decision modeling, and high-frequency actuation mechanisms. The findings indicate that integrating these distinct mechanisms into a unified “perception–decision–execution” architecture offers a means of balancing the trade-off between drift reduction and canopy coverage. By transitioning from broadcast applications to targeted delivery, this synergistic approach may contribute to improving pesticide utilization, minimizing off-target environmental contamination, and enhancing the biological efficacy of crop protection. Overall, this technical progression may facilitate the translation of precision spraying technologies from controlled laboratory validation to broader field implementation.

Binocular stereo vision is a low-cost and scalable 3D perception technology that shows strong potential in agricultural phenotyping and smart agriculture. By estimating depth from multi-view RGB images, it enables non-contact, high-precision sensing of crop structure, canopy morphology, growth dynamics, and livestock traits, providing essential support for digital and intelligent agricultural production. With recent advances in deep learning-based stereo matching, multimodal sensor fusion, and 3D reconstruction, its robustness and accuracy in complex field environments have been significantly improved. This paper systematically reviews recent progress in agricultural applications of binocular stereo vision, covering system architectures, traditional and deep learning-based stereo matching methods, point cloud reconstruction techniques, and emerging supervision strategies such as 3D Gaussian splatting. It further summarizes key applications, including high-throughput phenotyping, fruit localization and robotic harvesting, weed detection and precision spraying, autonomous navigation, and livestock body condition assessment, highlighting its role in multi-task agricultural perception systems. Finally, the paper discusses major challenges, including low-texture matching difficulty, occlusions in complex environments, cross-domain generalization, real-time lightweight deployment, and limited dataset availability. Future directions are outlined in foundation model-based visual perception, self- and weakly supervised learning, multimodal fusion, and edge-efficient model design, aiming to support large-scale deployment in smart agriculture.