## 3 Experiments and Results
### Field Experients  
#### 3.2.1 Verification Test of Performance  
Three equal-sized areas (A, B, and C) were established within the cultivation beds for system testing, each equipped with an independent storage tank and nutrient solution  
\begin{table}
\begin{tabular}{c c c c} \hline
**Control Method** & **Settling Time/s** & **Overshoot/\%** & **Steady-State Error/\({}^{\circ}\)C** \\ \hline PID & 200.36 & 24.50 & 0.035 \\ Fuzzy PID & 158.42 & 9.10 & 0.016 \\ DBO-Fuzzy PID & 35.23 & 2.18 & 0.009 \\ \hline \end{tabular}
\end{table}
Table 4: Evaluation indicators.  
Figure 16: Comparison of step response curves of three control methods with disturbance.  
Figure 15: Comparison of step response curves of three control methods without disturbance.  
circulation device. Area A served as the control group, with sensor nodes only deployed at the storage tank outlet to monitor nutrient solution temperature without cooling. Areas B and C were equipped with both sensor nodes and temperature control systems to enable intelligent regulation, validating the performance of traditional PID control and adaptive DBO-Fuzzy PID optimized cooling systems, respectively. According to previous studies [39], 22 \({}^{\circ}\)C represents the optimal nutrient solution temperature for NFT hydroponic lettuce cultivation. Figure 17 illustrates the nutrient solution temperature variations under typical summer conditions (8 August 2024).  
Figure 17 demonstrates that the average nutrient solution temperatures in areas B and C were 22.60 \({}^{\circ}\)C and 21.98 \({}^{\circ}\)C, respectively, both close to the optimal temperature (22 \({}^{\circ}\)C) for lettuce growth, consistent with expected cooling performance. However, during the high-temperature period from 11:00 to 17:00, the deviation from the optimal temperature increased in area B, indicating that traditional PID control lacks adaptive capability under extreme environmental variations. Area A maintained an average nutrient solution temperature of 32.76 \({}^{\circ}\)C, significantly higher than the optimal temperature. Figure 18 illustrates the deviation between the nutrient solution temperature and the optimal temperature (22 \({}^{\circ}\)C) for error analysis, with the vertical axis representing the temperature difference.  
Error analysis was conducted on the nutrient solution temperatures in areas B and C. Area C exhibited smaller average deviation and median values compared to area B. The narrower interquartile range and absence of outliers in area C's boxplot indicate that its nutrient solution temperature remained closer to the optimal value (22 \({}^{\circ}\)C), with a smaller variation range. Compared to traditional control methods, the smart nutrient solution temperature control system utilizing adaptive DBO-Fuzzy PID demonstrates superior performance, maintaining temperatures closer to the optimal range for lettuce growth. This system enables faster temperature adjustment, higher control precision, improved robustness, and enhanced practical applicability.  
Figure 17: Graph of nutrient solution temperature.  
Figure 18: Box plot of the difference between nutrient solution temperature and optimal temperature.