In order to real-time monitor the health status of pigs in the process of breeding and to achieve the purpose of early warning of swine respiratory diseases, the SE-DenseNet-121 recognition model was established to recognize pig cough sounds. The 13-dimensional MFCC, ΔMFCC and Δ²MFCC were transverse spliced to obtain six groups of parameters that could reflect the static, dynamic and mixed characteristics of pig sound signals respectively, and the DenseNet-121 recognition model was used to compare the performance of the six sets of parameters to obtain the optimal set of parameters. The DenseNet-121 recognition model was improved by using the SENets attention module to enhance the recognition model’s ability to extract effective features from the pig sound signals. The results showed that the optimal set of parameters was the 26-dimensional MFCC + ΔMFCC, and the rate of recognition accuracy, recall, precision and F1 score of the SE-DenseNet-121 recognition model for pig cough sounds were 93.8%, 98.6%, 97% and 97.8%, respectively. The above results can be used to develop a pig cough sound recognition system for early warning of pig respiratory diseases.

Introduction: Accurate application of pesticides at the seedling stage is the key to effective control of Chinese cabbage pests and diseases, which necessitates rapid and accurate detection of the seedlings. However, the similarity between the characteristics of Chinese cabbage seedlings and some weeds is a great challenge for accurate detection.Methods: This study introduces an enhanced detection method for Chinese cabbage seedlings, employing a modified version of YOLO11n, termed YOLO11-CGB. The YOLO11n framework has been augmented by integrating a Convolutional Attention Module (CBAM) into its backbone network. This module focuses on the distinctive features of Chinese cabbage seedlings. Additionally, a simplified Bidirectional Feature Pyramid Network (BiFPN) is incorporated into the neck network to bolster feature fusion efficiency. This synergy between CBAM and BiFPN markedly elevates the model’s accuracy in identifying Chinese cabbage seedlings, particularly for distant subjects in wide-angle imagery. To mitigate the increased computational load from these enhancements, the network's convolution module has been replaced with a more efficient GhostConv. This change, in conjunction with the simplified neck network, effectively reduces the model's size and computational requirements. The model’s outputs are visualized using a heat map, and an Average Temperature Weight (ATW) metric is introduced to quantify the heat map’s effectiveness.Results and discussion: Comparative analysis reveals that YOLO11-CGB outperforms established object detection models like Faster R-CNN, YOLOv4, YOLOv5, YOLOv8 and the original YOLO11 in detecting Chinese cabbage seedlings across varied heights, angles, and complex settings. The model achieves precision, recall, and mean Average Precision of 94.7%, 93.0%, and 97.0%, respectively, significantly reducing false negatives and false positives. With a file size of 3.2 MB, 4.1 GFLOPs, and a frame rate of 143 FPS, YOLO11-CGB model is designed to meet the operational demands of edge devices, offering a robust solution for precision spraying technology in agriculture.

To address the technical problems of broad droplet size spectrum, insufficient atomization uniformity, and spray drift in plant protection unmanned aerial vehicle (UAV) applications, this study developed a novel two-stage aerial electrostatic spraying device based on the coupled mechanisms of hydraulic atomization and electrostatic induction, and, through the integration of three-dimensional numerical simulation and additive manufacturing technology, a new two-stage inductive charging device was designed on the basis of the traditional hydrodynamic nozzle structure, and a synergistic optimization study of the charging effect and atomization characteristics was carried out systematically. With the help of a charge ratio detection system and Malvern laser particle sizer, spray pressure (0.25–0.35 MPa), charging voltage (0–16 kV), and spray height (100–1000 mm) were selected as the key parameters, and the interaction mechanism of each parameter on the droplet charge ratio (C/m) and the particle size distribution (Dv50) was analyzed through the Box–Behnken response surface experimental design. The experimental data showed that when the charge voltage was increased to 12 kV, the droplet charge-to-mass ratio reached a peak value of 1.62 mC/kg (p < 0.01), which was 83.6% higher than that of the base condition; the concentration of the particle size distribution of the charged droplets was significantly improved; charged droplets exhibited a 23.6% reduction in Dv50 (p < 0.05) within the 0–200 mm core atomization zone below the nozzle, with the coefficient of variation of volume median diameter decreasing from 28.4% to 16.7%. This study confirms that the two-stage induction structure can effectively break through the charge saturation threshold of traditional electrostatic spraying, which provides a theoretical basis and technical support for the optimal design of electrostatic spraying systems for plant protection UAVs. This technology holds broad application prospects in agricultural settings such as orchards and farmlands. It can significantly enhance the targeted deposition efficiency of pesticides, reducing drift losses and chemical usage, thereby enabling agricultural enterprises to achieve practical economic benefits, including reduced operational costs, improved pest control efficacy, and minimized environmental pollution, while generating environmental benefits.

Air induction nozzles possess good anti-drift performance, the throat and orifice sizes of the nozzles are main design parameters that affecting atomization. Therefore, Venturi tube nozzles and conventional flat fan nozzles were assembled together to investigate the flow rate, droplet size, the quantity of air in droplets affected by single design parameter of nozzles with applying high speed camera and Spraytec laser diffraction system. The results showed that: the flow rate of the air induction nozzle depended only on the throat size of Venturi tube and pressure, and it was proportional to the throat size of Venturi tube at the same pressure; The flat fan nozzle’s orifice size and Venturi tube size significantly affected volume median diameter of droplets, which generally increased after adding surfactant; A new model was established after optimizing classical equation for calculating the percentage of intake air in droplets and studying the effects of throat and orifice size of air induction nozzles on spray characteristics. By variance analysis, it was verified that the new model of quantity of air in droplets produced by all connected nozzles was correct. The calculation showed that the bubbles sizes ranged at 200-900 μm and were in proportion to the droplet size with the percentage of intake air of 10% to 90%. Contrast to the change of volume median diameter and droplet velocity, existence of intake air could influence their change degree to some extent. Keywords: throat orifice, spray characteristic, model of droplet motion, air induction nozzle DOI: 10.25165/j.ijabe.20211405.5513 Citation: Hu J, Liu C X, Wang Z C, Li Y F, Song J L, Liu Y J, et al. Motion model for describing the quantity of air in droplets through changing the structure of air induction nozzle. Int J Agric & Biol Eng, 2021; 14(5): 35–40.

The electrostatic spray technology can significantly improve the utilization rate of liquid medicine under the operation characteristics of unmanned aerial vehicles (UAVs) with small load and low spray volume. To explore the settlement law of electrostatic droplets, further improve the amount of droplets deposited in target crops, and reduce the loss of missing target, this study adopted the method of combining numerical simulation and high-speed photography to study the movement characteristics of electrostatic droplets of UAV induction conical electrostatic nozzle. Based on the droplet spatial dynamics theory, a user-defined function and volume of fluid (UDF-VOF) multiphase spray model is established to simulate the trajectory of electrostatic droplet. TEMA software is used to analyze the droplet motion image under electrostatic field, and the characteristic parameters, such as trajectory and velocity are obtained. Theoretical analysis and spray test results show that the main factors affecting electrostatic droplet settlement are charging voltage, droplet falling distance, and airflow velocity. The optimal charging voltage of electrostatic droplet is 14 kv, the maximum charge-mass ratio is 1.04 mC/kg, and the average particle size is 209.77 μm. The numerical simulation results show that spray height, charging voltage, and lateral wind speed have significant effects on droplet sedimentation. The results of high-speed camera analysis show that the induced electric field causes the droplet to adsorb the target crop, resulting in the droplet movement trajectory deflection.

Spraying is currently one of the main methods of pesticide application worldwide. It converts the pesticide solution into fine droplets through a sprayer, which then deposit onto target plants. Therefore, in the process of pesticide application, improving the effectiveness of spraying while minimizing or preventing crop damage has become a key issue. Combining the advantages of electrostatic spraying technology with the characteristics of ground boom sprayers, a contact-type electrostatic boom spraying system based on a rod–plate electrode structure was designed and tested on a self-propelled boom sprayer. The charging chamber was designed based on the characteristics of the rod–plate electrode and theoretical analysis. The reliability of the device was verified through COMSOL numerical simulations, charge-to-mass ratio, droplet size, and droplet size spectrum measurements, and a droplet size prediction model was established. The deposition characteristics in soybean fields were analyzed using the Box–Behnken experimental design method. The results showed that the rod–plate electrode structure demonstrated its superiority with a maximum spatial electric field of 2.31 × 10⁶ V/m. When the spray pressure was 0.3 MPa and the charging voltage was 8 kV, the droplet size decreased by 26.6%, and the charge-to-mass ratio reached 2.88 mC/kg. Field experiments showed that when the charging voltage was 8 kV, the spray pressure was 0.3 MPa, the traveling speed was 7 km/h, and the number of deposited droplets was 8517. This study provides some basis for the application of electrostatic spraying technology in large-scale field operations.