## 3 Results and Discussion
### Experimental Validation  
To test the control performance of various methods for remote conductivity control in a water and fertilizer integration machine, a potassium nitrate fertilizer solution was used. The testing device was positioned approximately 110 m from the field sensor section, with sensor sampling every 5 s, resulting in 180 sampling points over a total of 15 min. The EC target values were set at 1.4 mS/cm, 1.8 mS/cm, and 2.2 mS/cm in three separate groups. The control algorithms tested were PI, SMC, and VSMC. The initial EC value was approximately 1.0 mS/cm. Statistical analysis was performed using MATLAB to calculate steady-state EC ranges, settling times, and overshoot. Figure 11: Given angle curve. Figure 14: The variable-parameter sliding mode control (VSMC) strategy estimates the load simulation curve in time when the load changes. Figure 13: Speed response simulation curves of each control method. The comprehensive experimental data (Table 2) shows that in terms of remote conductivity control in water and fertilizer integration systems, the VSMC method performed the best. At a target electrical conductivity (EC) of 1.4 mS/cm, it achieved the smallest EC fluctuation range (0.204 mS/cm), shortest steady-state time (95 s), and lowest overshoot (14.3%). The SMC method exhibited an intermediate performance, with better results than PI but not as good as VSMC. For example, at a target EC of 1.4 mS/cm, the SMC method achieved an EC fluctuation range of 0.181 mS/cm, a steady-state time of 130 s, and an overshoot of 15.6%. These results suggest that SMC offers a balanced trade-off between the higher steady-state time and larger fluctuation observed with PI and the better performance seen with VSMC. In contrast, the PI control method performed the worst, exhibiting the largest EC fluctuation range (0.543 mS/cm), longest steady-state time (190 s), and highest overshoot (25.1%) at a target EC of 2.2 mS/cm. The experimental results of the control performance for the PI, SMC, and VSMC methods are shown in Figures 15-17, respectively. The PI control method demonstrated a varied performance across different target EC values. For a target EC of 1.4 mS/cm, the system achieved a steady-state EC range from 1.25 to 1.56 mS/cm, with a steady-state time of 160 s and an overshoot of 19.3%. When the target EC was set to 1.8 mS/cm, the steady-state EC ranged from 1.65 to 1.93 mS/cm, the steady-state time increased to 175 s, and the overshoot was 22.2%. At the highest target EC of 2.2 mS/cm, the steady-state EC varied from 2.10 to 2.30 mS/cm, with a steady-state time of 190 s and an overshoot of 25.1%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Control** & **Set EC** & **Steady-State** & **Steady-State** & **Overshoot (\%)** \\ \cline{2-5}
**Algorithm** & **1.4** & **1.25–1.56** & **160** & **19.3** \\ \cline{2-5}
**PI** & **1.8** & **1.65–1.93** & **175** & **22.2** \\ \cline{2-5}  & **2.2** & **2.10–2.30** & **190** & **25.1** \\ \hline \multirow{4}{*}{**SMC**} & **1.4** & **1.30–1.50** & **130** & **15.6** \\ \cline{2-5}  & **1.8** & **1.67–1.90** & **120** & **16.7** \\ \cline{2-5}  & **2.2** & **2.12–2.28** & **130** & **17.2** \\ \hline \multirow{4}{*}{**VSMC**} & **1.4** & **1.18–1.60** & **95** & **14.3** \\ \cline{2-5}  & **1.8** & **1.60–1.98** & **100** & **15.5** \\ \cline{1-1} \cline{2-5}  & **2.2** & **2.06–2.35** & **120** & **16.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance metrics of control algorithms for EC regulation. Figure 15: The control performance of the Proportional–Integral (PI) control method. The SMC method showed an improved performance compared to the PI control method. For a target EC of 1.4 mS/cm, the steady-state EC ranged from 1.30 to 1.50 mS/cm, with a steady-state time of 130 s and an overshoot of 15.6%. When the target EC was 1.8 mS/cm, the steady-state EC ranged from 1.67 to 1.90 mS/cm, with a reduced steady-state time of 120 s and an overshoot of 16.7%.