The canopy volume of fruit trees is an important input for the precise and varying application of pesticides in orchards. The fixed mesh division method is mostly used to calculate canopy volumes with variable target-oriented spraying. To reduce the influence of the working speed on the detection accuracy under a fixed mesh width division, the cuboid accumulation of divided areas (CADAs), which is a light detection and ranging (LiDAR) online detection method for a fruit tree canopy volume based on dynamic mesh division, is proposed in this paper. In the method, the area is divided according to the number of unilateral nozzles of the sprayer in the canopy height direction of the fruit tree, and the mesh width is dynamically adjusted according to the change in the working speed in the moving direction of the sprayer. To verify the accuracy and applicability of the method, the simulation canopy and peach tree canopy detection experiments were carried out. The test results show that the CADA method can be used to calculate the contour and volume of the canopy. However, detection errors easily occur at the edge of the canopy, resulting in a detection error of 8.33% for the simulated canopy volume. The CADA method has a good detection accuracy under different moving speeds and fruit tree canopy sizes. At a speed of 1 m/s, the detection accuracy of the canopy volume reaches 99.18%. Compared with the existing canopy volume calculation methods based on the alpha-shape algorithm and canopy meshing-profile characterization (CMPC), the detection accuracy of the CADA method is 2.73% and 7.22% better, respectively. This method can not only reduce the influence of the moving speed on the detection accuracy of the canopy volume, but also improve the detection accuracy. Thus, this method can provide theoretical support for the research and development of target-oriented variable spraying control systems for orchards.

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.

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.