## 1 Introduction  
Water and fertilizer integration technology is widely used in agricultural production, in which the concentration ratio and proper application of water and fertilizer are crucial to the growth of crops [1]. Currently, fertilizer and irrigation water use has gradually increased worldwide without a proportional increase in crop yields [2]. A qualitative analysis of fertilizer application methods shows that the main cause of this situation is the improper distribution of fertilizers, which affects the efficiency of fertilizer uptake and the normal growth of the crop at all stages. Excessive fertilizer application can also damage soil structure, affecting the growing environment and survival of crops [3; 4; 5; 6]. Therefore, in order to enhance the rationality of crop growth and the utilization rate of water and fertilizer resources, and to ensure the normal development and application of agricultural resources, more accurate and reasonable water and fertilizer blending integration technology should be adopted to realize the transformation of agricultural technology from being crude to intensive. The root systems of different crops require different levels of acidity and alkalinity, and most mono-textured fertilizers are alkaline; thus, the pH of the fertilizer needs to be explicitly adjusted at the fertilizer blending stage [7; 8]. However, the process of blending liquid fertilizer for pH is often affected by factors such as time lag and nonuniformity of fertilizer delivery, and the variation in liquid fertilizer pH is also nonlinear in nature. Therefore, fast and accurate adjustment of liquid fertilizer pH to set requirements is an important research area in precision agriculture [9; 10]. The research on pH values mainly includes algorithm optimization research and fertilizer system pH model building research. The current modeling research on pH process regulation mainly includes linear and nonlinear models and artificial intelligence models [11]. In the study of optimization of nonlinear control, methods such as adaptive regulation, feedback linearized transformation control and sliding mode control are generally used. Considering that the traditional PID control strategy has difficulty dealing with the complex nonlinearity and time lag in the control of water and fertilizer environments, more intelligent algorithms for optimal control need to be investigated [12]. At this stage, most water and fertilizer controllers use conventional PID control algorithms internally. However, in the actual fertilizer application process, the fertilizer concentration and flow rate have a complex impact on the water-fertil mixing system, which leads to problems such as time-varying and lagging system control objects always existing. The use of PID control algorithms is usually not effective for regulation [13; 14]. In view of this, the authors in [15] used the application of a fuzzy adaptive control to regulate the water-fertil system. The simulation results show that the fuzzy PID adaptive control method is able to track and regulate the dynamic changes of the nutrient preparation process more accurately than the traditional PID method. In [16], a single-neuron PID learning algorithm based on quadratic optimization was proposed for the strongly nonlinear pH process. The effectiveness of the algorithm was verified by the simulation control results of the strongly nonlinear pH process. In [17], a self-tuning fuzzy PID control algorithm was introduced into the control system to regulate the frequency of the fertilizer pump, and hence, the water and fertilizer through the fuzzy PID control algorithm. An expert system PID control was developed by the authors in [18]. The parameters of the controller can be adjusted according to the deviation of the pH value and the deviation rate. Simulation experiments showed that the expert PID control has good control performance.