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.

A method of soil moisture and organic matter content detection based on hyperspectral technology is proposed. A total of 800 different soil samples and hyperspectral data were collected in the laboratory and from the field. A hyperspectral database was established. After wavelet denoising and principal component analysis (PCA) preprocessing, the convolutional neural network (CNN) module was first used to extract the wavelength features of the data. Then, the long- and short-memory neural network (LSTM) module was used to extract the feature bands and nearby hidden state vectors. At the same time, the genetic algorithm (GA) was used to optimize the hyperparametric weight and bias value of the LSTM training network. At the initial stage, the data were normalized, and all features were analyzed by grey correlation degree to extract important features and to reduce the computational complexity of the data. Then, the GA-optimized CNN-LSTM hybrid neural network (GA-CNN-LSTM) algorithm model proposed in this paper was used to predict soil moisture and organic matter. The prediction performance was compared with CNN, support vector regression (SVR), and CNN-LSTM hybrid neural network model without GA optimization. The GA-CNN-LSTM algorithm was superior to other models in all indicators. The highest accuracy rates of 94.5% and 92.9% were obtained for soil moisture and organic matter, respectively. This method can be applied to portable hyperspectrometers and unmanned aerial vehicles to realize large-scale monitoring of moisture and organic matter distribution and to provide a basis for rational irrigation and fertilization in the future.

Minimizing the offloading latency of agricultural drip irrigation and fertilization tasks has long been a pressing issue in agricultural drip irrigation and fertilization wireless sensor networks (AIFWSNs). The introduction of edge computing as a robust and practical aid to cloud computing in AIFWSNs can significantly improve the execution speed of agricultural drip irrigation and fertilization tasks and effectively reduce the task offloading latency. Therefore, this paper investigates the optimization method of drip irrigation and fertilization task offloading delay in AIFWSNs based on edge computing and proposes a new edge task offloading method for AIFWSNs, namely, Quantum Chaotic Genetic Optimization Algorithm (QCGA). This paper introduces a novel quantum operator in QCGA, comprising a quantum non-gate and a quantum rotation gate, to improve the algorithm’s global search capability. The quantum operator accomplishes the updating of quantum rotating gates without querying the quantum rotation angle table, which reduces the computational complexity of introducing quantum optimization into the task offloading problem of AIFWSNs. This paper proposes a new chaotic operator to make the initial solution more uniformly distributed in the search space by chaotic mapping. This paper’s simulation experiments compared QCGA and snake optimizer (SO), genetic algorithm (GA), particle swarm optimization (PSO), sequential offloading, and random offloading methods. Simulation results showed that, compared with SO, GA, PSO, sequential offloading, and random offloading methods, the average delay of QCGA was reduced by 9.96%, 26.78%, 29.31%, 44.67%, and 61.24%.

Aiming at the problem of complex background interference and insufficient weld detection accuracy in steel structure weld recognition task, this paper proposes a target detection algorithm based on improved YOLOv5. By introducing the Coordinate Attention (CA) mechanism in the backbone network and integrating the CA with the C3 structure, the C3CA module is designed to effectively enhance the model’s perception of the spatial position of the weld and the detection accuracy of the weld. Experiments show that the improved YOLOv5s-C3CA model achieves 93.79% mAP@0.5 on the self-made weld data set, which is 2.48% higher than the original model. At the same time, the amount of model parameters is reduced by 8.53%, and the floating point operation is reduced by 10.6%, which achieves a balance between detection accuracy and calculation efficiency. This study verifies the effectiveness of the coordinate attention mechanism in improving the feature expression ability of the model, and provides a solution for the automatic detection of welds in industrial scenarios.

To improve the accuracy of calculating the aluminum electrolysis pre-close wind volume, this study focused on optimizing the two main factors that influence its magnitude: the aluminum output speed and the pre-close wind volume coefficient. First, the Unscented Kalman Filter (UKF) algorithm was used to estimate the aluminum output speed, and its application in real production was verified through simulation experiments. The results demonstrate that UKF provides more accurate speed estimates when handling the non-linear dynamic system of aluminum electrolysis. When there was a sudden change in speed, the UKF achieved a relative error of only 0.0373%, significantly lower than the 2.52% error of the traditional Kalman Filter (KF). At the same time, the UKF exhibited a shorter runtime in the simulation. Additionally, this research introduces a self-correction mechanism for the pre-close wind volume coefficient for the first time. By dynamically adjusting the parameter based on aluminum output deviations and applying the Average Filter (AF) to improve the compensation accuracy, the pre-close wind volume coefficient can be precisely calculated. The combination of these methods significantly enhances the accuracy and robustness of pre-close wind volume calculations, providing solid theoretical foundations and the technical support needed to achieve high-precision aluminum output control.

Addressing the issue of leakage in practical scenarios involving hose pumps, this paper conducts an analysis on hose pumps with existing compression gaps. The model is simplified to represent the two-dimensional Newtonian fluid pulsating flow of a single-sided sine wave passing through the tube. The study derives the axial flow velocity distribution within the tube while establishing correlations between the leakage volume, tube clearance, pressure differential, deformed cross-sectional length of the tube, rotor speed (which exhibits a negative correlation), the deformation length of the tube in the x-direction, and the fluid's dynamic viscosity (demonstrating a positive correlation). Utilizing a bidirectional fluid-structure coupling method, this analysis investigates hose pumps with compression gaps. Specifically, it examines the flow field pressure, velocity, and vortex intensity of two Newtonian fluids with Reynolds numbers of 10–30 and 6000–15 000, respectively, within a 180° bend tube with a curvature-to-inner-diameter ratio of 4.6. This analysis is conducted under inlet velocities of 0.32 and 0.6 m/s, and outlet pressures of 0 and 0.1 MPa. The study identifies the positions of extreme leakage rates, elucidating the flow field characteristics and their impact on leakage. Furthermore, it investigates the causes of secondary flows within the tube, concluding that the fluid inside the tube exhibits symmetric helical motion. This research establishes the periodic variations in flow field vortex intensity and secondary flow intensity, along with the conclusion that leakage rates are positively correlated with axial vortex intensity and negatively correlated with secondary flow intensity.