# Research on Grain Moisture Model Based on Improved SSA-SVR Algorithm
## 5 Conclusions  
Based on a self-made resistance-type grain moisture meter, this study involved placing long-grain rice into the moisture meter at a room temperature of 30 \({}^{\circ}\)C to obtain resistance values, voltage values, and corresponding moisture values. The voltage value obtained was considered the actual voltage value and used as the prediction set. Subsequently, the same batch of samples was placed into a PM-2500 model single-grain automatic moisture meter produced by the Japanese company Kett to obtain the moisture values of the grains. Based on these moisture values, the corresponding voltage value in the fitting curve of the homemade moisture meter was determined and considered the true voltage value and used as the modeling set. The improved SSA-SVR method was employed to train the grain moisture model. This algorithm combination can better handle time series data, extract useful features, and accurately predict grain moisture. Through model training, the grain moisture value can be quickly obtained directly from measuring the resistance value under certain temperature conditions, reducing the interference of environmental factors such as humidity and temperature on measurement results. This research may potentially improve the speed and accuracy of moisture measurement during on-site drying processes, as the model directly correlates resistance values to moisture values, reducing the influence of environmental factors compared to traditional moisture detection methods. If the model demonstrates stability under different environments and conditions, it will prove its strong applicability and broad potential for promotion [49]. Additionally, comparisons were made between models established using ridge regression, multilayer perceptron, random forest methods [50; 51], and the improved SSA-SVR method, showing that the latter exhibited superior performance in terms of correlation coefficient, determination coefficient, and RMSE. During the modeling process, the first challenge lies in the limitations of data quality and quantity. Initially, the sample preparation faced difficulties due to unfamiliar operational procedures, resulting in some waste and insufficient sample quantity. Subsequently, the same brand of long-grain rice was repurchased for sample preparation. Additionally, during the data collection process, there were some outliers that required preprocessing. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Correlation** & **Coefficient of** & & **Training** \\  & **Coefficient** & **Determination** & & **Time** \\ \hline SSA-SVR & 0.94 & 0.93 & 1.42 & 0.32 \\ (Improvement) SSA-SVR & 0.99 & 0.98 & 0.85 & 0.49 \\ RR & 0.92 & 0.86 & 3.84 & 0.57 \\ MLP & 0.94 & 0.93 & 2.99 & 3.84 \\ RF & 0.95 & 0.96 & 1.83 & 0.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of various models. While these issues are controllable, the moisture content of grains can fluctuate due to environmental conditions and storage methods, leading to inaccurate data, which is a drawback of this study. The second challenge pertains to the selection of parameters in the algorithm, such as the type of kernel function and regularization parameters. It is essential to find relatively suitable parameters for simulation training and choose the optimal parameters to prevent model overfitting or underfitting. The third limitation arises from the increased computational cost after introducing chaotic mapping and reverse learning mechanisms, requiring more iterations for convergence and extended training time. However, since the dataset is not large-scale, the impact of this aspect is relatively minor. Lastly, Support Vector Regression (SVR) is a black-box model with an opaque decision-making process, potentially limiting model interpretability and user trust. Moreover, SSA is used to extract the main components of time series data, but explaining the relationship between these components and grain moisture content is challenging, representing the major drawback of this study.