Yunwu Li

Air-assisted sprayers are an essential piece of equipment for improving spraying efficiency and pesticide utilization; their performance directly affects the effectiveness of pesticide application. This study, addressing the plant protection needs of hilly citrus orchards, designed an air duct structure for an air-assisted sprayer and analyzed its airflow characteristics and droplet deposition effects based on CFD simulation technology. The reliability of the simulation results was verified through air speed boundary tests, revealing that the maximum effective boundaries of the integrated air duct and the independent air duct in different directions were 18.4 cm and 17.2 cm, respectively, providing a reference for the spatial arrangement of the air duct. The study indicates that properly matching the fan speed, spray pressure, and spray distance could optimize droplet deposition, enhance spray uniformity, and improve pesticide utilization. However, excessively high fan speeds (>6000 r/min) or spray pressures (>0.8 MPa) may reduce droplet transport efficiency. This research provides theoretical support for the design and parameter optimization of sprayers in hilly citrus orchards.

To address the challenges of missed and false detections in citrus fruit detection caused by environmental factors such as leaf occlusion, fruit overlap, and variations in natural light in hilly and mountainous orchards, this paper proposes a citrus detection model based on an improved YOLOv5 algorithm. By introducing receptive field convolutions with full 3D weights (RFCF), the model overcomes the issue of parameter sharing in convolution operations, enhancing detection accuracy. A focused linear attention (FLA) module is incorporated to improve the expressive power of the self-attention mechanism while maintaining computational efficiency. Additionally, anchor boxes were re-clustered based on the shape characteristics of target objects, and the boundary box loss function was improved to Foal-EIoU, boosting the model’s localization ability. Experiments conducted on a citrus fruit dataset labeled using LabelImg, collected from hilly and mountainous areas, showed a detection precision of 95.83% and a mean average precision (mAP) of 79.68%. This research not only significantly improves detection performance in complex environments but also provides crucial data support for precision tasks such as orchard localization and intelligent picking, demonstrating strong potential for practical applications in smart agriculture.

The establishment of discrete element method (DEM) model for simulating the behavior of viscous soil can significantly contribute to the development of agricultural machinery, particularly in hilly areas worldwide. There is limited research on the parameter calibration of the DEM for purple soil in the Southwest China and its interaction mechanism between very narrow tine. Piling up test, rotating drum test and design-expert 12.0 software were used to design Plackett–Burman test, steepest climb test and Box–Behnken test successively. Combined with soil intrinsic parameter measurement test, soil simulation parameter calibration and optimization were completed. Based on the optimized DEM simulation parameters, the average relative errors of static repose angle and dynamic repose angle were 1.934 and 2.289%, respectively. The accuracy of parameter calibration results of DEM simulation was proved. Three kinds of very narrow tines were designed and processed. The interaction model and mechanism between soil-touch parts and soil was established. The average relative errors between the DEM simulation results of the forward resistance and disturbance area of the three soil-touch parts and the soil bin test results were 7.22% and 11.24%, respectively, which further proved that the calibration results of DEM parameters of purple soil and the interaction model with the soil-touch parts in this study were reliable. The physical and interaction parameters of slope cultivated purple soil in Southwest China were determined based on static-dynamic repose angle, providing more accurate parameters for study of soil-touch parts.

The reasonable allocation and control of CO₂ concentration in a greenhouse are very important for the optimal growth of crops. In this study, based on density functional theory (DFT), an MoS₂–GeSe monolayer was proposed to unravel the issues of the lower selectivity, poorer sensitivity and non-recyclability of traditional nanomaterial gas sensors. The incorporation of MoS₂ units greatly enhanced the sensitivity of the pure GeSe monolayer to CO₂ and the high binding energy also demonstrated the thermal stability of the doped structures. The ideal adsorption energy, charge transfer and recovery time ensured that the MoS₂–GeSe monolayer had a good adsorption and desorption ability. This paper aimed to solve the matter of recycling sensors within agriculture. This research could provide the theoretical basis for the establishment of a potentially new generation of gas sensors for the monitoring of crop growth.

To maximize the performance of power batteries and supercapacitors in a hybrid energy storage system (HESS) and to resolve the conflict between the high power demands of electric vehicles and the limitations of high-current charging and discharging of the power battery, a vehicle power demand model incorporating road slope information has been constructed. This paper takes a HESS composed of power battery and supercapacitor as the object, and a rule-based energy management strategy (EMS) based on road slope information is proposed to realize the reasonable distribution and management of energy under the slope condition. According to the slope information of the road ahead, the energy consumption in the next period was predicted, and the supercapacitor is charged and discharged in advance to meet the energy demand of uphill and the energy recovery capacity of downhill to avoid the high current charge and discharge of the battery. Subsequently, the improved EMS performance was simulated under the New York City Cycle (NYCC) driving conditions with additional slope driving conditions. The simulated results indicate that compared to the existing EMS, the proposed EMS based on slope information can effectively distribute the power demand between the power battery and the supercapacitor, can reduce the discharge current and the duration of high-power discharge, and has a 20.4% higher energy recovery efficiency, effectively increasing the cruising range.

This article deals with the problem of low participation of students as well as their poor understanding in the course of “Engine Principle”. In this paper, the current situation and characteristics of the curriculum are analyzed. With the advantages of MOOC, the authors tend to explore blended learning model, which is composed of online knowledge obtaining and offline flipped class. The design and practice of the model is implemented in three aspects: teaching contents, class organization and assessment. We recorded teaching videos and related course materials based on knowledge points and uploaded them to the Mooc platform. Tests run through the whole training process in each stage in order to evaluate the effect of blended learning mode. This blended learning model has balanced traditional teaching and modern teaching advantage. Furthermore, the ability of students to put what they have learned into practice and the ability to innovate has been promoted. With the blended learning mode based on MOOC, it is a better way to solve the problems of low participation and poor understanding and application ability of students in the current teaching.

Hilly areas necessitate a field road vehicle with high automation to reduce the amount of labor required to transport agricultural products and to increase productivity. In this paper, an adaptive integrated navigation method (combining global navigation satellite system (GNSS) and inertial navigation system (INS)) and path tracking control strategy of field road vehicles are studied in view of the problems of frequent GNSS outages and high automatic control precision requirement in hilly areas. An indirect Kalman filter (KF) is designed for the GNSS/INS information fusion. A modified method for calculating the KF adaptive factor is proposed to effectively suppress the divergence of the KF and a threshold judgement method to abandon the abnormal GNSS measurement is proposed to deal with GNSS interruptions. To achieve automated driving, a five-layer fuzzy neural network controller, which takes the lateral deviation, heading deviation, and path curvature as input and the steering angle as output, is proposed to control vehicle autonomous tracking of the navigation trajectory accurately. The proposed system was evaluated through simulation and experimental tests on a field road. The simulation results showed that the adjusted KF fusion algorithm can effectively reduce the deviation of a single GNSS measurement and improve the overall accuracy. The test results showed the maximum deviation of the actual travel trajectory from the expected trajectory of the vehicle in the horizontal direction was 12.2 cm and the average deviation was 5.3 cm. During GNSS outages due to obstacles, the maximum deviation in the horizontal direction was 12.7 cm and the average deviation was 6.1 cm. The results show that the designed GNSS/INS integrated navigation system and trajectory tracking control strategy can control a vehicle automatically while driving along a field road in a hilly area.