The pulse-width modulation (PWM) variable spray system is the most widely used variable spray system in the world at present, which has the characteristics of a fast response, large flow adjustment range, and good atomization. Recently, the pressure fluctuation and droplet deposition uniformity of the PWM variable spray system caused by the intermittent spray mode of the nozzle have attracted more and more attention. In this study, a method for eliminating the inhomogeneity of ground deposition in low-frequency PWM variable sprays based on a staggered-phase drive mode was proposed, and a PWM variable spray system was built. The experimental results indicated that the pressure fluctuation amplitude upstream of the nozzle of the PWM variable spray system with the staggered-phase drive was reduced by 40.91%, and the dispersion rate of the pressure fluctuation was reduced by 62.78% (the initial pressure was 0.3 MPa, solenoid valve frequency was 5 Hz, and duty cycle was 50%). The PWM control parameters had a significant effect on the upstream pressure fluctuation (initial pressure > duty cycle > frequency). The droplet spectrum relative span of the staggered phased PWM variable spray system decreased by 24.83%, the coefficient of variation of the droplet particle size decreased by 4.40%, the particle size was more uniform, and the atomization effect was improved. The average deposition of droplets in the forward direction driven by the staggered phase was 4.87% greater than that in the same phase, and the variation rate decreased by 20.87%. The average deposition amount increased, and the deposition became more uniform. Staggered-phase spray control could effectively reduce the inhomogeneity of deposition in low-frequency PWM intermittent spraying. This research provides strong technical support for a precision variable spraying effect and droplet drift prevention.

Orchard spraying can effectively control pests and diseases. Over-spraying commonly results in excessive pesticide residues on agricultural products and environmental pollution. To avoid these problems, variable spraying technology uses target canopy detection to evaluate the leaf area in a canopy and adjust the application rate accordingly. In this study, a mobile LiDAR detection platform was set up to automatically measure point cloud data for a thick canopy in an apple orchard. A test platform was built, and manual measurements of the canopy leaf area were taken. Then, polynomial regression, back propagation (BP) neural network regression, and partial least squares regression (PLSR) algorithms were used to study the relationship between the orchard tree canopy point clouds and leaf areas. The BP neural network algorithm (86.1% and 73.6% accuracies for the test and verification data, respectively) and the PLSR algorithm (78.46% and 60.3%, respectively) performed better than the Fourier function of the polynomial regression (59.73% accuracy). The leaf area model obtained using PLSR was intuitive and simple, while the BP neural network algorithm was more accurate and could meet the requirements for high-precision variable spraying.

Orchard pesticide off-target deposition and drift cause substantial soil and water pollution, and other environmental pollution. Orchard target-oriented spraying technologies have been used to reduce the deposition and drift caused by off-target spraying and control environmental pollution to within an acceptable range. Two target-oriented spraying systems based on photoelectric sensors or ultrasonic sensors were developed. Three spraying treatments of young cherry trees and adult apple trees were conducted using a commercial sprayer with a photoelectric-based target-oriented spraying system, an ultrasonic-based target-oriented spraying system or no target-oriented spraying system. A rhodamine tracer was used instead of pesticide. Filter papers were fixed in the trees and on the ground. The tracer on the filter papers was washed off to calculate the deposition distribution in the trees and on the ground. The deposition data were used to evaluate the systems and pesticide off-target deposition achieved with orchard target-oriented sprayers. The results showed that the two target-oriented spraying systems greatly reduced the ground deposition compared to that caused by off-target spraying. Compared with that from off-target spraying, the ground deposition from photoelectric-based (trunk-based) and ultrasonic-based (canopy-based) target-oriented spraying decreased by 50.63% and 38.74%, respectively, for the young fruit trees and by 21.66% and 29.87%, respectively, for the adult fruit trees. The trunk-based target-oriented detection method can be considered more suitable for young trees, whereas the canopy-based target-oriented detection method can be considered more suitable for adult trees. The maximum ground deposition occurred 1.5 m from the tree trunk at the back of the tree canopy and was caused by the high airflow at the air outlet of the sprayer. A suitable air speed and air volume at the air outlet of the sprayer can reduce pesticide deposition on the ground.

To address the limitations of traditional GNSS-based navigation systems in orchard environments, we propose a multi-sensor fusion-based autonomous navigation method for orchards. A crawler-type agricultural platform was used as a test vehicle, and an autonomous orchard navigation system was constructed using a 2D LiDAR, a dynamic electronic compass, and an encoder. The proposed system first filters LiDAR point cloud data and uses the DBSCAN–ratio–threshold method to process data and identify clusters of tree trunks. By matching the center coordinates of trunk clusters with a fruit tree distribution map, the platform’s positional measurements are determined. An extended Kalman filter fusion algorithm is then employed to obtain a posterior estimate of the platform’s position and pose. Experimental results demonstrate that in localization accuracy tests and navigation tests, the proposed system provides high navigation accuracy and robustness, making it suitable for autonomous walking operations in orchard environments.

This study addresses the challenge of segmenting strawberry leaves and lesions in natural backgrounds, which is critical for accurate disease severity assessment and automated dosing. Focusing on strawberry powdery mildew, we propose an enhanced YOLOv8-based segmentation method for leaf and lesion detection. Four instance segmentation models (SOLOv2, YOLACT, YOLOv7-seg, and YOLOv8-seg) were compared, using YOLOv8-seg as the baseline. To improve performance, SCDown and PSA modules were integrated into the backbone to reduce redundancy, decrease computational load, and enhance detection of small objects and complex backgrounds. In the neck, the C2f module was replaced with the C2fCIB module, and the SimAM attention mechanism was incorporated to improve target differentiation and reduce noise interference. The loss function combined CIOU with MPDIOU to enhance adaptability in challenging scenarios. Ablation experiments demonstrated a segmentation accuracy of 92%, recall of 85.2%, and mean average precision (mAP) of 90.4%, surpassing the YOLOv8-seg baseline by 4%, 2.9%, and 4%, respectively. Compared to SOLOv2, YOLACT, and YOLOv7-seg, the improved model’s mAP increased by 14.8%, 5.8%, and 3.9%, respectively. The improved model reduces missed detections and enhances target localization, providing theoretical support for subsequent applications in intelligent, dosage-based disease management.

Precision pesticide application mainly relies on canopy volume, resulting in varied application effectiveness across different density areas of orchard trees. This study examined pesticide application effectiveness based on the spray wind, canopy volume, and leaf area within the canopy, providing variable bases for precise regulation of spray wind and pesticide dosage. The study addresses the knowledge gap by utilizing laser detection and ranging (LiDAR) to measure the thickness and leaf area of orchard tree canopies. The spray experiments were conducted on canopies of different regions, using an air-assisted sprayer with varying fan speeds of 1381 r/min, 1502 r/min, and 1676 r/min. The deposition effects were analyzed using water-sensitive papers. The inlet air speed within the canopy did not increase proportionally when the spray fan speed increased, and it showed a significant variation in locations with sparse foliage. Furthermore, droplets exhibited abnormal median volume diameters of the canopy regions with lower wind loss rates and smaller leaf areas. The influences were in the order of canopy thickness, leaf area, and inlet air speed on the cumulative deposition of droplets on both sides of the water-sensitive papers, as well as the ratio of deposition between the two sides, from big to small, are inlet air speed, leaf area, and canopy thickness. The study provides a scientific foundation for air control in precision pesticide application in apple orchards and contributes to the rapid development of precision spraying technologies.