Lixin Zhang

The feeder, a critical component of pneumatic conveying and distributing fertilizer machines for cotton, significantly impacts cotton quality and yield. This study addresses the limitations of conventional feeders, namely poor delivery stability and insufficient air–fertilizer mixture uniformity, by introducing a novel feeder design incorporating guide-flow chamber vanes. Based on the structural principles of pneumatic fertilizer distribution and the theory of vortex flow feeding, the new feeder design was developed and analyzed using a CFD-DEM coupled model to simulate its operational process. Response surface methodology was employed to optimize key structural parameters, aiming to enhance both air–fertilizer mixture uniformity and fertilizer delivery stability. The optimized design features two guide vanes, a fertilizer discharge contraction angle of 23.2°, and eight chamber lines. Validation through simulation confirmed the model’s reliability, with a discrepancy of less than 5% between simulated and predicted values. Experimental validation on a pneumatic fertilizing test prototype further demonstrated the effectiveness of the optimization, resulting in a feeder stability coefficient of variation of 13.30% and a coefficient of variation in fertilizer distribution across the system’s branches of 3.96%, satisfying practical operational requirements. After optimization, the stability and uniformity of the feeder fertilizer are significantly improved. The optimized feeder design effectively improves the stability and air–fertilizer mixing uniformity of cotton pneumatic fertilizing machines, providing valuable theoretical and technical support for their design optimization and performance enhancement.

The efficient operation of smart farms relies on the precise monitoring of farm environmental information, necessitating the deployment of a large number of wireless sensors. These sensors must be integrated with their specific locations within the fields to ensure data accuracy. Therefore, efficiently and rapidly determining the positions of sensor nodes presents a significant challenge. To address this issue, this paper proposes a hybrid optimization DV-Hop localization algorithm based on the chaotic crested porcupine optimizer. The algorithm leverages the received signal strength indicator, combined with node hierarchical values, to achieve graded processing of the minimum number of hops. Polynomial fitting methods are employed to reduce the estimation distance error from the beacon nodes to unknown nodes. Finally, the chaotic optimization crested porcupine optimizer is designed for intelligent optimization. Simulation experiments verify the proposed algorithm’s localization performance across different monitoring areas, varying beacon node ratios, and assorted communication radii. The simulation results demonstrate that the proposed algorithm effectively enhances node localization accuracy and significantly reduces localization errors compared to the results for other algorithms. In future work, we plan to consider the impact of algorithm complexity on the lifespan of wireless sensor networks and to further evaluate the algorithm in a pH monitoring system for farmland.

In an effort to improve the soil covering effect of tree planting machines and reduce the lodging rate of seedlings, this paper analyzes the soil covering process and working principle of the soil covering device of tree planting machines and designs a soil covering device with adjustable soil covering width. With the help of the discrete element simulation software EDEM, the orthogonal test was designed with the width of the covering soil and the inclination angle as the test factors and the soil backfill rate as the response index. The regression analysis of the test results was carried out by Design-expert, and the optimal parameter combination of the covering soil device was determined as follows: the width of the covering soil: 600 mm, the forward angle: 45°, and the depth of the soil: 20 mm. The prototype verification test was carried out under the optimal parameters, and the soil backfill rate was 88.4%, which was basically consistent with the optimized parameters. The soil covering device with adjustable width designed in this study is of great significance to improve the soil covering rate of tree planting machines and reduce the lodging rate of seedlings.

Simulation and structural parameter optimization of rotary blade cutting soil based on SPH method

In order to mitigate time-varying, lag, and nonlinearity impacts on fertilization systems and achieve precise control of liquid conductivity, we propose a novel hybrid-optimized fractional-order proportional-integral-derivative (PID) algorithm. This algorithm utilizes a fuzzy algorithm to tune the five parameters of the fractional-order PID algorithm, employs the Smith predictor for structural optimization, and utilizes Wild Horse Optimizer, improved by genetic algorithms, to optimize fuzzy rules. We conducted MATLAB simulations, precision experiments, and stability tests on this controller. MATLAB simulation results, along with precision experiment results, indicate that compared to PID controllers, Smith predictor-optimized PID controllers, and fuzzy-tuned fractional-order PID controllers, the proposed controller has the narrowest steady-state conductivity range, the shortest settling time, and the lowest overshoot, showcasing excellent overall dynamic performance. Stability test results demonstrate that the controller maintains stable operation under different pressure conditions. Therefore, this control system from our study achieves superior control effectiveness, providing a viable approach for the control of nonlinear time-delay systems.

Xinjiang is the largest cotton-producing region in China, but it faces a severe shortage of water resources. According to relevant studies, the cotton yield does not significantly decrease under appropriate limited water conditions. Therefore, this paper proposes a water and fertilizer integrated control system to achieve water and fertilizer conservation in the process of cotton field cultivation. This paper designs a fuzzy neural network Proportional–Integral–Derivative controller based on the improved Wild Horse Optimizer to address the water and fertilizer integrated control system’s time-varying, lag, and non-linear characteristics. The controller precisely controls fertilizer electrical conductivity (EC) by optimizing parameters through an improved Wild Horse Optimizer for the initial weights from the normalization layer to the output layer, the initial center values of membership functions, and the initial base width of membership functions in the fuzzy neural network. The performance of the controller is validated through MATLAB simulation and experimental tests. The results indicate that, compared with conventional PID controllers and fuzzy PID controllers, this controller exhibits excellent control accuracy and robustness, effectively achieving precise fertilization.

The reference crop evapotranspiration (ET₀) statistic is useful for estimating agricultural system water requirements and managing irrigation. In dry areas, the accurate calculation of ET₀ is crucial for optimal agricultural water resource utilization. By investigating the relationship between meteorological information and ET₀ in Shihezi City, four prediction models were developed: a BP neural network prediction model, a BP neural network prediction model improved by genetic algorithm (GA-BP), a BP neural network prediction model improved by particle swarm algorithm (PSO-BP), as well as an improved hybrid BP neural network prediction model (GA-PSO-BP). The Pearson correlation analysis found that the key parameters influencing ET₀ were temperature (T_max, T_ave, T_min), hours of sunshine (N), relative humidity (RH), wind speed (U), as well as average pressure (AP). Based on the analysis results, different combinations of meteorological input factors were established for modeling, and the results showed that when the input factors were temperature (T_max, T_ave, T_min), hours of sunshine (N), as well as relative humidity (RH), the overall effect of the ET₀ prediction model was better than the other input combinations, and the GA-PSO-BP prediction model was the best, which could provide some guidance for the deployment and use of water resources. This may assist in the allocation and utilization of agricultural water resources in Shihezi.

China’s field crops such as cotton, wheat, and tomato have been produced on a large scale, but their cultivation process still adopts more traditional manual fertilization methods, which makes the use of chemical fertilizers in China high and causes waste of fertilizer resources and ecological environmental damage. To address the above problems, a hybrid optimization of genetic algorithms and particle swarm optimization (GA–PSO) is used to optimize the initial weights of the backpropagation (BP) neural network, and a hybrid optimization-based BP neural network PID controller is designed to realize the accurate control of fertilizer flow in the integrated water and fertilizer precision fertilization control system for field crops. At the same time, the STM32 microcontroller-based precision fertilizer application control system for integrated water and fertilizer application of large field crops was developed and the performance of the controller was verified experimentally. The results show that the controller has an average maximum overshoot of 5.1% and an average adjustment time of 68.99 s, which is better than the PID and PID control algorithms based on BP neural network (BP–PID) controllers; among them, the hybrid optimization of PID control algorithm based on BP neural network by particle swarm optimization and genetic algorithm(GA–PSO–BP–PID) controller has the best-integrated control performance when the fertilizer application flow rate is 0.6m³/h.

The rotary tillage knife roller, as one of the typical soil-touching parts of the tillage equipment cutting process, is in direct contact with the soil. During the cutting process, there are problems related to structural bending, deformation, and high power consumption, caused by impact and load, and it is difficult to observe the micro-change law of the rotary tillage tool and soil. In view of the above problems, we took the soil of the cotton experimental field in Shihezi, Xinjiang, and the soil-contacting parts of the rotary tillage equipment, specifically the rotary tiller roller, as the research subject. Using the finite-element method (FEM) to simulate the structure of the rotary tiller with different bending angle parameters, we obtained its average stress and deformation position information, and obtained a range linear relationship between the bending angle and the structural performance of the rotary tiller tool. Using discrete element method (DEM)-based simulation to build the corresponding contact model, soil particle model, and soil–rotary tillage knife roll interaction model to simulate the dynamic process of a rotary tillage knife roll cutting soil, we obtained the change rules of the soil deformation area, cutting process energy, cutting resistance, and soil particle movement. By using the orthogonal simulation test and the response surface method, we optimized the kinematic parameters of the rotary tiller roller and the key design parameters of a single rotary tiller. Taking the reduction of cutting power consumption as the optimization goal and considering the influence of the bending angle on its structural performance, the optimal parameter combination was obtained as follows: the forward speed was 900 m/h, the rotation speed was 100 rad/min, the bending angle was 115°, and the minimum power consumption of the cutter roller was 0.181 kW. The corresponding average stress and deformation were 0.983 mm and 41.826 MPa, which were 15.8%, 13%, and 7.9% lower than the simulation results of power consumption, stress, and deformation under the initial parameter setting, respectively. Finally, the effectiveness of the simulation optimization model in reducing power consumption and the accuracy of the soil-cutting simulation were verified by a rotary tilling inter-field test, which provided theoretical reference and technical support for the design and optimization of other typical soil-touching parts of tillage and related equipment, such as disc harrow, ploughshare, and sub-soiling shovel.