Weeds significantly impact crop yields and quality, necessitating strict control. Effective weed identification is essential to precision weeding in the field. Existing detection methods struggle with the inconsistent size scales of weed targets and the issue of small targets, making it difficult to achieve efficient detection, and they are unable to satisfy both the speed and accuracy requirements for detection at the same time. Therefore, this study, focusing on three common types of weeds in the field—Amaranthus retroflexus, Eleusine indica, and Chenopodium—proposes the HAD-YOLO model. With the purpose of improving the model’s capacity to extract features and making it more lightweight, this algorithm employs the HGNetV2 as its backbone network. The Scale Sequence Feature Fusion Module (SSFF) and Triple Feature Encoding Module (TFE) from the ASF-YOLO are introduced to improve the model’s capacity to extract features across various scales, and on this basis, to improve the model’s capacity to detect small targets, a P2 feature layer is included. Finally, a target detection head with an attention mechanism, Dynamic head (Dyhead), is utilized to improve the detection head’s capacity for representation. Experimental results show that on the dataset collected in the greenhouse, the mAP for weed detection is 94.2%; using this as the pre-trained weight, on the dataset collected in the field environment, the mAP for weed detection is 96.2%, and the detection FPS is 30.6. Overall, the HAD-YOLO model has effectively addressed the requirements for accurate weed identification, offering both theoretical and technical backing for automatic weed control. Future efforts will involve collecting more weed data from various agricultural field scenarios to validate and enhance the generalization capabilities of the HAD-YOLO model.

In the Huang-Huai-Hai region of China, the instability of electric boom sprayers has prompted many farmers to raise the boom height to improve clearance. However, the drift risks associated with these conditions remain poorly assessed. This study investigated two key factors influencing drift: boom height and nozzle type. The standard LI CHENG VP11003 nozzle was compared to the Teejet XR11003 nozzle, and droplet size and velocity were measured at various boom heights. The results showed that, at the same boom height, the LI CHENG nozzle produced droplets with an average D[V, 0.5] 14.6 µm larger (8.13%), an average velocity 0.53 m/s lower (29.26%), and a relative span (RS) value 0.05 higher (4.52%) compared to the Teejet nozzle. Drift tests were performed under field conditions using a spray drift test bench. The results showed that the total drift amount per unit area (TDA) for the LI CHENG nozzle showed minimal variation at boom heights of 0.4–0.6 m (Stage 1), 0.7–0.9 m (Stage 2), and 1.0–1.2 m (Stage 3). The drift potential of the LI CHENG VP11003 nozzle increased by 136.62% in Stage 2 and 282.69% in Stage 3, relative to Stage 1. Similarly, the Teejet XR11003 nozzle showed increases of 30.52% and 165.51% in Stages 2 and 3, respectively. The results showed that the LICHENG nozzle, which is the standard equipment on the sprayer, can only be used to moderately increase the boom height to improve the sprayer’s clearance within the range of the first stage. When the boom height exceeds this range, the drift risk becomes too high. This study provides meaningful insights into enhancing drift control and developing application strategies for growers.

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.

In the dynamic operation of a boom sprayer, the boom oscillation will cause the detection value of the boom height to fluctuate greatly, resulting in failures of the control system. Based on the previously developed static boom height detection model for the entire wheat growth cycle, this study aimed to optimize the model to reduce the impact of boom oscillation on the accuracy of boom height detection. Three ultrasonic sensors were installed on each section boom of a three-section boom sprayer, and dynamic boom height detection tests were conducted at vehicle speeds of 4 to 8 km/h across six growth stages of winter wheat in Beijing, a total detection area within a single fixed operational row of approximately 14 ha. The test results showed that as vehicle speed increased, boom oscillations intensified across all sections. By setting the boom oscillation correction parameters, the detecting value of each section of boom height is corrected. The results show that the fluctuation and deviation degree of the boom height-detecting value are obviously reduced, and the correction effect is obvious. Further analysis of the detecting value of the boom height after the correction shows that the previously established detection model still maintains high detection accuracy under dynamic conditions; that is, the detection position of the ultrasonic sensor does not downward shift. This paper provides a low-cost technical method that can be directly applied to the dynamic detection of boom height.

Microwave irradiation is a new means of non-toxic, residue-free, and green soil disinfection that prevents and controls soil diseases, insects, and weeds and helps to improve crop quality and yield. Soil microorganisms, as an important part of the ecosystem, are closely related to crop growth and health. To investigate the changes of soil physicochemical properties and microbial communities during microwave soil disinfection for different time periods, phaeozems from northeastern China were selected for microwave treatment at 3, 6, 9, and 12 min, and their physicochemical properties were measured after 30 days of incubation. The test soils (0–20 cm) after 30 days of incubation were used, and high-throughput sequencing was performed to detect changes in their soil microbial structure under different microwave time treatments. Microwave treatment had significant effects on soil pH, nitrate (NO₃^--N), ammonium (NH₄⁺-N), and available phosphorus (AP) content. As shown by the Shannon, Chao, and Ace indices, microwave treatment at 3 min had the lowest effect on bacterial diversity compared to the control treatment (CK). Shannon index decreased by 9.92%, 24.56%, 34.37%, and 38.43% after 3, 6, 9, and 12 min microwave treatments, respectively; Chao index decreased by 7.69%, 18.13%, 32.21%, and 57.91%, respectively; Ace index decreased by 6.40%, 6.98%, 20.89%, and 52.07%, respectively. The relative abundance of beneficial soil microorganisms Micromonospora, Fictibacillus, Paenibacillus, and Bacillus (Firmicutes) increased significantly compared to CK. The results indicated that although the microwave treatment altered the soil microbial community, beneficial soil microorganisms showed faster recovery. In addition, pH, soil organic carbon (SOC), total nitrogen ratio (C/N), soil-available phosphorus (AP), and NO₃^--N were important factors affecting bacterial community diversity and composition following microwave treatment, and bacterial community composition was driven by soil chemical properties such as soil pH, SOC, C/N, and NO₃^--N. Microwave treatments at different time periods affected soil microbial community structure to different degrees, and soil bacteria of copiotrophic taxa (e.g., Firmicutes) were relatively higher than the control. Overall, microwave treatment from 3–6 min may be more suitable for soil disinfection. The study of the effect of microwave on soil physicochemical properties and bacterial microbial community not only provides some scientific reference for the rational application of microwave soil disinfection, but also has positive significance for soil-borne disease control and crop quality improvement.

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.