Precision pesticide application is a key focus in orchard management, with targeted spraying serving as a core technology to optimize pesticide delivery and reduce environmental pollution. However, its accurate implementation relies on high-precision environmental sensing technologies to enable the precise identification of target objects and dynamic regulation of spraying strategies. This paper systematically reviews the application of orchard environmental sensing technologies in targeted spraying. It first focuses on key sensors used in environmental sensing, providing an in-depth analysis of their operational mechanisms and advantages in orchard environmental perception. Subsequently, this paper discusses the role of multi-source data fusion and artificial intelligence analysis techniques in improving the accuracy and stability of orchard environmental sensing, supporting crown structure modeling, pest and disease monitoring, and weed recognition. Additionally, this paper reviews the practical paths of environmental sensing-driven targeted spraying technologies, including variable spraying strategies based on canopy structure perception, precise pesticide application methods combined with intelligent pest and disease recognition, and targeted weed control technologies relying on weed and non-target area detection. Finally, this paper summarizes the challenges faced by multi-source sensing and targeted spraying technologies in light of current research progress and industry needs, and explores potential future developments in low-cost sensors, real-time data processing, intelligent decision making, and unmanned agricultural machinery.

With the ongoing advancement of global agricultural modernization, intelligent technologies have gained significant attention in agricultural production—particularly in the field of intelligent orchard sprayers, where notable progress has been achieved. Intelligent orchard sprayers, equipped with precise sensing and control systems, enable targeted spraying. This enhances the efficiency of crop health management, reduces pesticide usage, minimizes environmental pollution, and supports the development of precision agriculture. This review focuses on three core modules of intelligent sprayer technology: perception and intelligent control, spray deposition and drift control, and autonomous navigation with system integration. By addressing key areas such as sensor technologies, object detection algorithms, and real-time control strategies, this review explores current challenges and future directions for intelligent orchard sprayer technology. It also discusses existing technical bottlenecks and obstacles to large-scale adoption. Finally, this review highlights the pivotal role of intelligent orchard sprayer technology in enhancing crop management efficiency, improving environmental sustainability, and facilitating the transformation of agricultural production systems.

Air-assisted spraying is vital in modern orchard pest management by enhancing droplet penetration and coverage on complex canopies. However, the interaction between airflow, droplets, and flexible foliage remains unclear, limiting spray efficiency and environmental sustainability. This review summarizes recent advances in understanding leaf motion dynamics in wind and droplet fields and their impact on pesticide deposition. First, we review orchard spraying technologies, focusing on air-assisted systems and their contribution to more uniform coverage. Next, we analyze mechanisms of droplet deposition within canopies, highlighting how wind characteristics, droplet size, and canopy structure influence pesticide distribution. Special attention is given to leaf aerodynamic responses, including bending, vibration, and transient deformation induced by wind and droplet impacts. Experimental and simulation studies reveal how leaf motion affects droplet retention, spreading, and secondary splashing. The limitations of static boundary models in deposition simulations are discussed, along with the potential of fluid-structure interaction (FSI) models. Future directions include integrated leaf-droplet experiments, intelligent airflow control, and incorporating plant biomechanics into precision spraying. Understanding leaf motion in spray environments is key to enhancing orchard spraying efficiency, precision, and sustainability.

The fruit industry is a significant economic sector in China, with modern orchards gradually transitioning to trellis orchards. For mechanized orchard operations, automatic vehicle navigation is essential. However, in trellis orchards, the shading from trees results in low average light intensity and large variations in lighting, posing challenges for path navigation. To address this, a path navigation algorithm for trellis orchards is proposed based on the UNet-CBAM model. The network structures of UNet, FCN, and SegNet are compared to identify and select the optimal structure for further improvement. Among the three attention mechanisms of channel attention, spatial attention, and combined attention, the most effective mechanism is identified. The optimal attention mechanism is incorporated into the optimized network to enhance the model’s ability to detect path edges and improve detection performance. To validate the effectiveness and generalizability of the model, a total of 400 images were collected under varying lighting intensities. The experimental results show that this method achieves an accuracy of 97.63%, a recall of 93.94%, and an Intersection over Union (IoU) of 92.19%. These results significantly enhance path recognition accuracy in trellis orchards, particularly under low light under conditions. Compared to the FCN and SegNet algorithms, this method provides higher detection accuracy and offers a new theoretical foundation and research approach for path recognition in low-light environments.

To address practical issues, such as uneven fertilizer distribution and poor particle dispersion, during the operation of a centrifugal double-disc fertilizer spreader, a discrete element method (DEM) simulation was initially performed. In this simulation, the blade inclination angle, fertilizer discharge offset angle, and spreading height were selected as experimental factors. The spreading width and the coefficient of variation (C_v) of fertilizer uniformity served as evaluation indicators. A quadratic orthogonal simulation experiment was designed to evaluate spreading performance. Subsequently, regression equations were established to optimize parameters and determine the optimal design configuration. Experimental results showed optimal performance with a blade inclination angle of −5°, fertilizer discharge offset angle of 45°, spreading height of 1050 mm, and disc rotational speed of 400 rpm. Considering that fertilizer spreaders are susceptible to air resistance and environmental wind during field operations, an EDEM-CFD coupling method was employed to simulate realistic operating conditions. Virtual simulation results demonstrated optimal fertilizer spreading performance at disc rotational speeds ranging from 350 to 400 rpm under tailwind conditions, and from 400 to 500 rpm under headwind conditions. Based on the simulation results, a prototype was built and tested. The experimental results closely matched the simulation predictions, thereby confirming the effectiveness of the simulation model. This study provides valuable insights and serves as a reference for designing and optimizing fertilizer spreader performance under practical operational conditions.

With the advancement of precision agriculture, variable-rate spraying (VRS) technology has demonstrated significant potential in enhancing pesticide utilization efficiency and promoting environmental sustainability, particularly in orchard applications. As a critical medium for pesticide transport, the dynamic structural characteristics of orchard canopies exert a profound influence on spraying effectiveness. This review systematically summarizes recent progress in the dynamic perception and modeling of orchard canopies, with a particular focus on key sensing technologies such as LiDAR, Vision Sensor, multispectral/hyperspectral sensors, and point cloud processing techniques. Furthermore, it discusses the construction methodologies of static, quasi-dynamic, and fully dynamic canopy modeling frameworks. The integration of canopy sensing technologies into VRS systems is also analyzed, including their roles in spray path planning, nozzle control strategies, and precise droplet transport regulation. Finally, the review identifies key challenges—particularly the trade-offs between real-time performance, seasonal adaptability, and modeling accuracy—and outlines future research directions centered on multimodal perception, hybrid modeling approaches combining physics-based and data-driven methods, and intelligent control strategies.