## 4 Conclusions  
This study developed and validated a variable-structure model control (VSMC) method for water and fertilizer integration systems, effectively addressing nonlinear remote conductivity disturbances and system parameter variability. By incorporating a recursive least squares observer, the proposed system dynamically adjusts electrical conductivity (EC) control, significantly improving the precision of irrigation and fertilization processes.  
Simulations revealed that VSMC outperformed the conventional PI and standard SMC methods in response speed, settling time, and disturbance rejection. In step response tests, VSMC achieved the target EC in 0.07 s, compared to 0.14 and 0.12 s for PI and SMC, respectively. VSMC also showed a higher tracking accuracy and better robustness under variable load conditions, markedly reducing steady-state errors.  
Field experiments further confirmed these findings. When tested at target EC values of 1.4, 1.8, and 2.2 mS/cm, VSMC consistently achieved more stable and accurate EC regulation than PI and SMC. Regional irrigation trials demonstrated that VSMC significantly improved fertilizer distribution uniformity, lowering the standard deviation of EC from 2.14 (PI control) to 0.59. This mitigated fertilizer over- and under-application across subplots, supporting more sustainable crop management.  
Regarding practical implementation, large-scale deployment of the VSMC system requires several prerequisites, as follows: (1) integration with high-precision EC sensors and stable actuator systems, (2) real-time communication infrastructure for feedback control, and (3) adaptive parameter tuning tools for varying soil and environmental conditions. Furthermore, the modularization of control units and compatibility with existing irrigation infrastructure will be key to scalability.  
Beyond demonstrating a superior control performance, this research offers valuable insights for designing advanced adaptive control strategies in precision agriculture. Futurestudies will focus on multi-zone irrigation applications, real-time soil moisture integration, energy optimization, and the application of machine learning to enhance sliding surface parameter adaptation.  
In conclusion, the VSMC approach will significantly enhance EC regulation in integrated water and fertilizer systems. It presents a scalable and adaptive solution with a high potential for real-world agricultural deployment, aiming to improve crop productivity, optimize input usage, and reduce environmental footprint.  
Conceptualization, P.Z. and L.Z.; methodology, P.Z.; software, L.Z.; validation, P.Z., Z.L., and L.Z.; formal analysis, L.Z. and X.H.; investigation, L.Z. and X.H.; resources, L.Z. and X.H.; data curation, P.Z.; writing--original draft preparation, P.Z. and L.Z.; writing--review and editing, P.Z. and L.Z.; visualization, P.Z.; supervision, P.Z.; project administration, L.Z.; funding acquisition, L.Z. and X.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Major Science and Technology Special Project of Xinjiang Uygur Autonomous Region, No. 2022A02012-4. This research was funded also by the Corps Science and Technology Plan Project, No. 2023A0B05. This research was funded also by the XPCC Science and Technology Plan Project, No. 2022 BC004.  
Not applicable.  
All relevant data presented in the article are stored according to institutional requirements and, as such, are not available online. However, all data used in this manuscript can be made available upon request to the authors.  
The authors declare no conflicts of interest.