## 1 Introduction
The authors in [19] studied and established a grey prediction model for water and fertilizer irrigation and used MATLAB software to simulate fuzzy PID control in irrigation and fertilizer application, which effectively improved the accuracy of water and fertilizer concentration according to experimental verification. The authors in [20] used quantitative feedback theory, a particle swarm optimization algorithm and a genetic algorithm to optimize PID control. The nonlinear problem of pH control was optimized. Additionally, the control performance was verified by simulation experiments. A wavelet-BP neural network-based method for accurate fertilizer application to maize was proposed in [21]. The accuracy of the optimal fertilizer amount was improved by establishing a wavelet-BP neural network to calculate the nonlinear problem of fertilizer application. The authors in [22] used an incremental PID algorithm to control the water and fertilizer ratios and constructed a simulation model with online parameter settings using RBF neural networks. According to the experimental verification, the RBF-PID model was more accurate and stable than the incremental PID model. The authors in [23] proposed a nonlinear model predictive control method based on an elastic BP neural network and hybrid grey wolf optimizer. Simulation results showed that the proposed controller performed well and could reduce the disturbance caused by nonlinearity. In summary, the main work carried out in this paper to improve the accuracy and rapidity of fertilizer pH regulation, based on water and fertilizer integration, is as follows: (a) An improved neural network controller is proposed which improves control by fuzzifying the inputs and optimizing the network parameters. (b) Particle swarm algorithms are applied for the optimization of two-layer weight parameters of neural networks. (c) PID control, fuzzy control, fuzzy neural network PID control and the control proposed in this paper are simulated and verified on the MATLAB/Simulink platform, and it is concludedthat the algorithm proposed in this study is better than other control algorithms. (d) In order to verify the practicality and reliability of the proposed algorithm, the experimental verification of the data acquisition, the transfer function, and the upper computer reading and decision controls are designed in this paper, and the results prove that the control algorithm proposed in this paper has better results and performance compared with other control methods. The paper is structured as follows. The first portion of this paper analyses the pH regulation process and develops a mathematical model of water and fertilization through the equilibrium equation. In the second part, a fuzzy preprocessing controller and a neural network controller are constructed, and the neural net controller is optimized using a particle swarm algorithm. The second part firstly constructs the controller that fuzzily preprocesses the input signal, then constructs the neural network controller, and finally uses the particle swarm algorithm to optimize the neural network controller. The third part compares the control effects of the four control algorithms through software simulation and experimental verification, and finally obtains the verification results based on the water and fertilizer system in this study.