Crop cultivation in solar greenhouses is affected by issues such as low levels of automation in spraying machinery, inefficient spraying, and a lack of suitable spraying equipment for vertically cultivated crops, all of which are in urgent need of resolution. To address these problems, this paper proposes a suspended-rail automatic variable-distance targeted spray system. The rated working speed of the spray system is 0.3 m/s, and the rated working pressure is 0.3 MPa. This system achieves precise spraying of crops at varying heights by dynamically adjusting the position of the spray nozzle. To ensure accuracy in spraying, the system employs a laser ranging sensor for real-time measurement of crop positions and spraying distances. Combined with a parameter processing scheme, the system generates control signals to adjust the operation of the electric push rod and electromagnetic valve, thereby dynamically adjusting the spraying distance and timing. Using vertically cultivated potted peppers as experimental subjects, this study compares the performance of fixed- and variable-distance spraying modes. The results indicate that, compared to the fixed-distance mode, the automatic variable-distance mode increases pesticide adherence by 16.65% and reduces pesticide usage by 29.58%. The proposed suspended-rail automatic variable-distance targeted spray system offers an effective technical solution for the precise spraying of vertically cultivated crops in solar greenhouses and thus contributes to improved pesticide utilization efficiency, reduced pesticide residue, and lower environmental pollution.

In response to the current low work efficiency of soil ridge-working machinery, as well as its poor stability, passability, and adaptability, this paper designs an independent leg-type working platform that can autonomously adjust its vehicle attitude through LiDAR scanning in a soil ridge-working environment. The platform, in terms of its mechanism and structural design, adopts dual parallelogram mechanisms, dual lead screw mechanisms, and independent column leg mechanisms, with a maximum adjustable ground clearance of 107 mm and a maximum wheelbase adjustment of 150 mm. A gyroscope is mounted at the center of the platform for attitude adjustment, ensuring the accurate data collection of the ultrasonic ranging module. Moreover, the platform adopts an adaptive adjustment method based on vehicle attitude and soil ridge shape parameters, obtaining soil ridge parameters through LiDAR and combining ultrasonic ranging module data with stepper motor pulse signals to obtain the absolute vehicle attitude parameters, using first and second linear regression methods to adjust the vehicle attitude and other working parameters. A prototype was also created, and the test data from the soil obtained through experiments show that, after leveling with the gyroscope leveling algorithm, the average value of the pitch angle is up to 0.6154°, and the average value of the roll angle is up to 0.9989°, with the maximum variance of the pitch angle being 0.0474° and the maximum variance of the tilt angle being 0.1320°. After the ultrasonic ranging module data are filtered by the Kalman filter, the maximum variance is 0.0304, and after applying the final fusion algorithm, the maximum variance is only 0.0085. The LiDAR measurement width value deviates from the actual width value by no more than 1.0 cm, and the LiDAR measurement height value deviates from the actual height value by no more than 1.0 cm. The platform’s actual adjusted width deviates from the actual soil ridge width by no more than 2.0 cm, and the platform’s actual adjusted height deviates from the actual soil ridge height by no more than 1.2 cm. This platform can improve the passability, adaptability, and stability of agricultural machinery in soil ridge work and provide technical references for subsequent related research.

The uniform and accurate mixing of pesticides in water is a necessary prerequisite for plant protection, especially for enabling precise variable spraying, and is also an important method to achieve a precise reduction in pesticide spraying. In order to ensure the uniform mixing of pesticides and water and solve the problems of traditional injection mixers, such as the limited range in the mixing ratio and unadjustable proportion, an active injection liquid mixer is designed in this paper. The mixer can be matched with an online mixing and spraying device to achieve accuracy in mixing and spraying. In this paper, a computational fluid dynamics (CFD) method is used to optimize the structure of the mixer. Through comparative analysis, the optimal structure of the mixer was found. It has a spherical head and conical tail, the number of guide plates is seven, and the shape is semicircular. By calculating the volume fraction of pesticide distribution under different cross-sections, the coefficient of variation in the process of mixing is obtained. The analysis shows that the maximum coefficient of variation of the ball-head cone-tail active injection mixer was 2.88% (lower than the allowable 5%) with a mixing ratio ranging from 300:1 to 3000:1. At the same time, image analysis methods of high-definition photography and ultraviolet spectrophotometry were used to analyze the mixing effect of the mixer. The test results show that, when the pressure of the pesticide injection is 1 MPa, the distribution of the pesticide and water in the ball-head cone-tail injection mixer is more uniform under different mixing ratios, and it has a better spatio-temporal distribution uniformity with the concentration changing a little at different times and different spatial locations. The mixer can provide a theoretical reference and technical support for the subsequent realization of an accurate online variable spray.

We aim to address the insufficient robustness of navigational line detection for rapeseed seed production sires in complex field scenarios and the challenges faced by existing models in balancing precision, real-time performance, and resource consumption. Taking YOLOv8n-seg as the baseline, we first introduced the ADown module to mitigate feature subsampling information loss and enhance computational efficiency. Subsequently, the DySample module was employed to strengthen target feature representation and improve object discrimination in complex scenarios. Finally, the c2f module was replaced with c2f_FB to optimise feature fusion and reinforce multi-scale feature integration. Performance was evaluated through comparative experiments, ablation studies, and scenario testing. The model achieves an average precision of 99.2%, mAP50-95 of 84.5%, a frame rate of 90.21 frames per second, and 2.6 million parameters, demonstrating superior segmentation performance in complex scenarios. SegNav-YOLOv8n balances performance and resource requirements, validating the effectiveness of the improvements and providing reliable technical support for navigating agricultural machinery in rapeseed seed production.