During variable-rate spraying in orchards, the atomization characteristics and distribution of droplets in and out of the target area can be affected by the sprayer pressure. In this study, a variable-rate spraying control system test bench was designed, and a hollow cone nozzle QY82.317.22 was selected. The droplet atomization characteristics, including volume median diameter (D_v0.5), the relative span of the droplet spectrum, and droplet velocity at different spray pressures, were studied at distances ranging from 0.4 to 2.4 m from the nozzle orifice with an air velocity of 10 m/s at the nozzle orifice position. The effects of longitudinal distance, transverse distance, and spray pressure on D_v0.5, relative span, and droplet velocity were analysed by multiple linear regression analysis, and the regression model was established. The experimental results show that at a longitudinal distance of 1.8 m, D_v0.5 ranges from 120 to 150 μm, meeting the requirements for optimal droplet size for controlling crawling pests and plant diseases on crop leaves; and the relative span is 1.2, indicating a wide droplet spectrum. At different pressure conditions, D_v0.5 decreases as pressure increases. Through multiple linear regression analysis, the longitudinal distance, the transverse distance, and the spray pressure have high significance for D_v0.5 and the droplet velocity. The longitudinal distance and the transverse distance have a highly significant effect on the relative span. In this study, the mathematical relational model of droplet characteristics at different spatial positions and different pressures was established, providing an agricultural reference for predicting the droplet characteristics at different spatial positions to achieve the best application effect. This model is conducive to the effective use of pesticides and reduces environmental pollution.

Field studies were conducted in the North China Plain (NCP) during the 2023–2024 season to investigate the vertical microclimate, yield, and yield-related characteristics of winter wheat during the grain-filling stage under no-till direct seeding and conventional tillage. The aim was to compare the differences in microclimate between the two tillage methods in wheat fields and the impact of microclimate on yield. The results indicated that, compared to conventional tillage, no-till direct seeding reduced the air temperature and increased the relative humidity of the air at 20 cm and 100 cm above the ground during the wheat grain-filling period. The soil moisture content at 20 cm below the ground under no-till direct seeding was higher than under conventional tillage during the early grain-filling stage. Seven days before the wheat harvest, the dry weight per plant and the dry weight per spike were significantly greater under no-till direct seeding than under conventional tillage. Consequently, the thousand-grain weight of no-till direct seeding was significantly higher than that of conventional tillage, with an increase of 7.9%. The number of wheat sterile spikelets under no-till direct seeding was significantly lower than that under conventional tillage. Furthermore, the number of grains per spike was higher than that of conventional tillage. Although the number of harvested spikes under no-till direct seeding was 10.8% lower than under conventional tillage, the increase in thousand-grain weight and the number of grains per spike compensated for the reduced number of harvested spikes. As a result, the grain yield of winter wheat under no-till direct seeding was higher than that of conventional tillage, increasing by 2.7%. Therefore, adopting no-till direct seeding in the NCP is conducive to increasing winter wheat production and efficiency, as well as supporting sustainable agricultural development.

Soil-borne diseases and continuous cropping obstacles have become critical factors constraining sustainable agricultural development. Traditional pesticide application methods commonly suffer from uneven dosage distribution, significant chemical waste, and environmental pollution. To enhance the precision and system stability of soil disinfection, this paper designs a precision pesticide application system for soil disinfection based on the Particle Swarm Optimization Proportional-Integral-Derivative algorithm (PSO-PID). Centered on a C37 controller, the system integrates the application pipeline, pumps, electric ball valves, multiple sensors, and a control terminal. The PSO-PID algorithm was deployed on the Codesys V2.3 platform, achieving precise flow control by adjusting electric ball valve openings in conjunction with velocity feedback. Simulink simulations showed that the PSO-PID algorithm outperforms conventional PID control in terms of settling time, overshoot, and steady-state error. Bench tests further validated the effectiveness of the proposed algorithm. Compared to conventional PID, the PSO-PID algorithm demonstrates superior control accuracy, faster response time, and enhanced application stability, with relative errors of 2.33%, 1.25%, and 1.20% respectively, while those of the conventional PID algorithm reach 3.67%, 3.35% and 4.88% respectively, representing a reduction of approximately 50% compared with the conventional PID algorithm. The results of the system application uniformity test indicated that the PSO-PID algorithm reduces relative error by approximately 40% compared to conventional PID, with the coefficients of variation being 2.02%, 1.73% and 1.81% respectively, which represented a significant improvement over those of the conventional PID algorithm (3.36%, 3.13% and 3.81%). Both application uniformity and stability outperform conventional PID algorithms, effectively minimizing application deviation. It outperforms conventional PID in both application uniformity and stability, effectively minimizing application deviation. The findings demonstrate that the proposed PSO-PID application control method achieves high control accuracy and application stability, providing reliable technical support for precision soil disinfection application.

Underground pests are difficult to monitor because of their hidden activity, and traditional manual surveys are inefficient. Computer vision provides a promising approach for efficient detection; however, pest boundaries and textures in complex soil backgrounds can be obscured or confounded by soil particles, residual roots, shadows, and other factors, thereby reducing detection accuracy. In this study, an image dataset of grubs, cutworm larvae, and cutworm pupae was constructed, and a lightweight detection model, HFU-YOLO, was proposed using YOLO11n as the baseline. A Haar Wavelet Downsampling (HWD) module was introduced to reduce the loss of shallow edge and texture information. A Lightweight Frequency-Adaptive Dilated Convolution (LFADC) module was designed to enhance target feature representation and reduce soil background interference. A UGS-Lite auxiliary loss function was adopted to improve the localization stability of weak-boundary targets. The results showed that HFU-YOLO achieved Precision, Recall, mAP@0.5, and mAP@0.5:0.95 values of 77.6%, 78.3%, 83.5%, and 44.1% on the test set, respectively, representing improvements of 4.2, 3.2, 6.1, and 6.1 percentage points over YOLO11n. With 2.59 M parameters and 6.2 GFLOPs, HFU-YOLO provides a reference for intelligent recognition of underground pests in complex soil backgrounds.