The orchard air-assisted sprayer exhibits strong droplet penetration, allowing uniform adhesion on both the front and back surfaces of leaves. The spray deposition and spray drift are influenced not only by the amount of pesticide but also by the airflow provided by the air-assisted system. To meet the requirements for regulating airflow and pesticide application in orchards, a method based on the iris structure for continuous adjustment of the air inlet area was proposed. An airflow control mechanism for orchard air-assisted sprayers, which is easy to install, was developed, and a circular recirculating pressure-stabilized spray system was designed. Additionally, an orchard sprayer supporting graded airflow control was developed. Experiments were conducted to assess airflow variation within the tree canopy and the spray deposition and spray drift under different airflow levels. The results showed that the average airflow attenuation rates in the canopy were 63.0%, 71.8%, and 82.5% for the leaf areas and canopy thicknesses of 826.5 cm², 1409.8 cm², and 1931.1 cm², and 1.5 m, 2.0 m, and 2.5 m, respectively. Graded variable control of airflow based on canopy variation helps improve droplet uniformity within the canopy and reduce spray drift in non-target areas. When the airflow in the middle of the tree canopy exceeds 1.50 m/s, the spray deposition ratio on both sides of the leaves increases to 83.55%, and the coefficient of variation in the droplet deposition on both sides is less than 33.24%. These findings provide support for pesticide precision application and spray drift control in orchards.

Multi-outlet air-assisted sprayers are increasingly used for directional and zoned airflow to match varying canopy structures. In this study, a self-developed multi-outlet orchard air-assisted sprayer was investigated. Airflow velocity and direction were tested at different inlet areas, heights, and downstream horizontal distances using a three-dimensional ultrasonic anemometer. Analysis of variance (ANOVA) and regression modeling were applied to elucidate the effects of these three factors on airflow velocity, horizontal angle (θ), and elevation angle (Φ). The results showed that a stable alternating “primary jet–interaction zone” structure was formed in the spatial airflow field under all operating conditions, indicating that the fundamental airflow pattern was mainly governed by the sprayer layout. Varying the inlet area did not alter the basic airflow structure; however, the intensity and directional stability of the primary jets were significantly modified. Larger inlet openings produced higher airflow velocities, with a maximum near-field velocity of 19.7 m s⁻¹, whereas smaller inlet openings resulted in faster far-field attenuation and more pronounced diffusion. Increasing the inlet area caused the θ distribution peak to converge toward 0°, thereby improving axial coherence and directional stability. In contrast, decreasing the inlet area shifted Φ toward more negative values, with Φ reaching approximately −20° in the far field; moreover, far-field differences in Φ were more pronounced. Under the minimum inlet opening area condition (S1), the airflow velocity within the region 80–100 cm from the outlet can be stably maintained above 3 m/s, with a relatively uniform velocity distribution. This is beneficial for improving droplet deposition uniformity within the canopy and reducing droplet drift in non-target areas. Based on the experimental data, a regression model for mean airflow velocity was established (R² = 0.873), demonstrating good predictive performance and indicating that inlet-opening regulation is feasible. These findings provide a basis for airflow matching and spray-parameter optimization for different canopy structures.

Orchard wind-assisted spraying technology relies on auxiliary airflow to disturb the canopy and improve droplet deposition uniformity. However, there are few effective means of quantitatively assessing the dynamic response of fruit tree leaves to airflow or the changes in airflow patterns within the canopy in real time. To address this, this study proposed an online monitoring method for the aerodynamic characteristics of fruit tree leaves using strain gauge sensors. The flexible strain gauge was affixed to the midribs of leaves from peach, pear and apple trees. Leaf deformations were captured with high-speed video recording (100 fps) alongside electrical signals in controlled wind fields. Bartlett low-pass filtering and Fourier transform were used to extract frequency-domain features spanning between 0 and 50 Hz. The AdaBoost decision tree model was used to evaluate classification performance across frequency bands. The results demonstrated high accuracy in identifying wind exposure (98%) for pear leaf and classifying the three leaf types (κ = 0.98) within the 4–6 Hz band. A comparison with the frame analysis of high-speed video recordings revealed a time error of 2 s in model predictions. This study confirms that strain gauge sensors combined with machine learning could efficiently monitor fruit tree leaf responses to external airflow in real time. It provides novel insights for optimizing wind-assisted spray parameters, reconstructing internal canopy wind field distributions and achieving precise pesticide application.

To address the limitations of conventional point-based sampling methods (e.g., water-sensitive paper and filter paper) in characterizing the three-dimensional deposition distribution on curved fruit surfaces, this study proposed a fruit-surface deposition quantification approach validated through a coupled workflow comprising multi-view 3D reconstruction, target point cloud segmentation, and chemical wash-off quantification. A carmine solution was used as a tracer to simulate pesticide deposition. High-accuracy colored point clouds of apples were generated via multi-view 3D reconstruction, and the target deposition point cloud was automatically extracted using a combined strategy integrating K-means-based coarse color clustering with HSV threshold refinement. The target point cloud count was regressed against the measured single-fruit deposition volume. The results showed a significant linear relationship between the target point cloud count and deposition under both laboratory and real orchard air-assisted spraying conditions (laboratory: R² = 0.827, r = 0.909, n = 13, p < 0.01; orchard: R² = 0.885, r = 0.941, n = 17, p < 0.01). This method can characterize the differences in pesticide deposition among fruits while preserving the three-dimensional location information of the tracer-related region, thus providing a methodological basis for characterizing pesticide deposition on the fruit surface.

Efficient plant protection requires precise monitoring of spray droplets, yet current in situ methods for measuring in-flight droplet flow are limited. This study proposed a laser imaging-based method to quantify spray intensity without physical contact or tracers. An optimal imaging angle was determined via simulation by maximizing the linearity between the received optical feature and droplet volume density while satisfying geometric constraints. A compact acquisition device was then developed and tested with eight nozzle specifications under fixed pressure. Image processing algorithms—including cropping, RGB channel separation, and binarization—were employed to extract pixel area and cumulative intensity, with gravimetric measurements serving as the reference. Results showed that under optimized exposure and gain settings, features from the green and blue channels exhibited a strong linear correlation with flow rate (R² = 0.93–0.97). Based on these findings, this study demonstrates that in-flight droplet flow rate can be directly quantified from image features—a departure from conventional deposition-based approaches. The proposed method enables rapid, non-contact spray assessment using only a camera and laser module, offering a low-cost, simple-structured solution for spray system optimization and field monitoring.