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.

Due to the time-varying, hysteresis and nonlinear characteristics of fertilizer concentration control in the water–fertilizer ratio control system, common control algorithms such as PID and fuzzy PID cannot obtain the expected control effect. In order to accurately control the cotton field water–fertilizer ratio regulation system drip irrigation process of the water–fertilizer ratio that will be controlled within a reasonable range, it is needed to design a bat-optimized variable-domain fuzzy PID water–fertilizer ratio control strategy, through the use of bat algorithm to find out the optimal expansion factor and the best domain of the current conditions, and then according to the changes in working conditions to automatically adjust the fuzzy control of the domain, through the control of the valve openings to change the fertilizer pump back to the amount of water. Realize the fast and precise control of fertilizer concentration in the water–fertilizer ratio control system. Comparative tests were conducted to verify the traditional PID, fuzzy PID, variable domain fuzzy PID and bat-optimized variable-domain fuzzy PID control algorithms. The results show that: if the water–fertilizer ratio is adjusted to 50:1 from the startup, the adjustment time required to reach the target water–fertilizer ratio under the bat-optimized variable-domain fuzzy PID control is 15.29 s, and the maximum overshooting amount is 16.28%, which is a smaller adjustment time and overshooting amount; if the water–fertilizer ratio is adjusted to 40:1 from 50:1, the advantages of bat-optimized variable-domain fuzzy PID are more obvious, with the best balance of response speed, overshooting amount and optimal control effect. In terms of response speed, overshooting amount and regulation time, the optimal balance is achieved, showing the optimal control effect. It is proved that the performance of the water–fertilizer ratio regulation system in cotton field under bat-optimized variable-domain fuzzy PID control designed in this paper can meet the actual production requirements, and these findings can help to develop precise irrigation technology for cotton cultivation under drip irrigation conditions.

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.

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.