## 3 Results and Discussion
### Validation Experiment  
To verify the reliability of the mathematical model of moisture content prediction, the model was written into the microcontroller of the online moisture content measurement device. The moisture content value of corn during drying was predicted online, and the predicted value was compared to the measured value. The verification experiment of online moisture content measurement was also conducted at Zhencheng Farm. The moisture content measurement device continuously collected corn temperature and capacitance values during the drying process, and Equation (23) was used to calculate the online moisture content value without porosity compensation. The sensor full of corn was removed every 30 min and placed into the porosimeter to measure the porosity of the corn, and Equation (24) was used to calculate the moisture content value after porosity compensation. A few corns were sampled at the outlet of the grain hopper of dryer, and the moisture content of the samples was determined by the standard oven method [13]. The calculated results were compared to the online measurement results of moisture content online device simultaneously. The experimental results are shown in Figure 8.  
As shown in Figure 8, the online moisture content measurement value is in good agreement with the offline measurement value as a whole, and the dynamic change trend in the moisture content and porosity with drying time is consistent. In the early stage of drying, the fluctuations in the online and offline measurement values were both relatively large. When the moisture content was greater than 22.45% w.b., the absolute error of measured moisture content was between \(-\)2.4% and 1.2%. The moisture content of corn decreased gradually with the drying process, and the absolute error of moisture content measurement also decreased gradually. When the moisture content was below 17.43% w.b., the measured moisture content value without porosity compensation was basically consistent with the value after porosity compensation, with a deviation of \(-\)0.4-0.6%. In the whole drying process, the online moisture content value without porosity compensation fluctuated widely, with an absolute error of \(-\)2.4% to 0.6% and an average absolute error of 0.79%. After porosity compensation, the fluctuation in the online moisture content value significantly reduced. The absolute error of moisture content measurement was \(-\)0.62% to 0.67%, and the average of absolute values of error was 0.32%, which is more accurate than  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Moisture Content** & **Temperature** & **Porosity** & **Predicted** & **Measured** & **Relative Error** \\  & **(\%w.b.)** & **(\%C)** & **(\%)** & **Capacitance (pF)** & **Capacitance (pF)** & **(\%)** \\ \hline
1 & 15.2 & 5 & 38.8 & 1.03 & 1.09 & 5.5 \\
2 & 15.2 & 20 & 43.1 & 1.52 & 1.64 & 7.31 \\
3 & 20.6 & 30 & 43.2 & 3.82 & 4.16 & 8.11 \\
4 & 25.4 & 15 & 42.1 & 4.03 & 4.19 & 3.79 \\
5 & 25.4 & 25 & 42.1 & 4.95 & 5.31 & 6.86 \\
6 & 20.6 & 10 & 39.1 & 2.83 & 2.93 & 3.42 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of validation experiments.  
the device (absolute error of \(\pm\)0.4%) developed by [PERSON] et al. [14]. Therefore, by introducing the quantitative porosity parameter, the variation law of the capacitance in the filling layer can be more comprehensively revealed. The online measurement accuracy of moisture content in the drying process was also significantly improved, especially for grain with high moisture content, which is crucial for the data feedback for closed-loop and optimal control in the initial stage of the drying process.