# Wheat Quantity Monitoring Methods Based on Inventory Measurement and SVR Prediction Model
## 4 Experimental Analysis  
_Construction of Training and Test Samples_  
Since the SVR prediction model has the advantage of obtaining the optimal solution in a restricted sample, it is particularly suitable for the analysis and prediction of small-sample problems. In this study, a total of 232 sets of samples were collected for model training and testing. We randomly selected 182 samples as training samples, and the remaining 50 samples were used as test samples. A partial sample of the amount of established wheat stocks is given in Table 1. The sample characteristics selected in this study include the initial moisture content (IMC), the measured moisture content (MMC), the measured bulk density (MBD), the storage time (ST), the measured net volume (MNV), the storage book weight (SBW), and the empirical weight (MW). To effectively evaluate the performance of SVR prediction models, we designed SVR prediction models based on linear kernel, radial basis kernel, and Sigmoid kernel, respectively, under the condition that other parameters were kept constant. The evaluation indexes given by Formulas (21) and (22) were used to evaluate the performance of different kernel function SVR prediction models. The smaller the mean squared error E, the better the performance of the SVR prediction model. The closer the determined coefficients were to the value 1, the better the SVR prediction model. Otherwise, the SVR prediction model performed worse. It can be seen from Figures 3 and 4 that the mean square errors of the \begin{table}
\begin{tabular}{c c c c c c c} \hline
**IMC (\%)** & **MMC (\%)** & **MBD (g\(\cdot\) L\({}^{-1}\))** & **ST (Month)** & **MNV (m\({}^{3}\))** & **SBW (kg)** & **MW (kg)** \\ \hline
12.3 & 11.6 & 764 & 29 & 318.019 & 246,422 & 246,108.7427 \\
12.3 & 11.6 & 764 & 24 & 1371.176 & 1,113,928 & 1,092,281.901 \\
12.3 & 11.6 & 764 & 24 & 192.4163 & 149,740 & 148,790.7688 \\
12.3 & 11.7 & 764 & 18 & 4449.902 & 3,486,050 & 3,467.868.607 \\
12.5 & 11.2 & 767 & 22 & 3783.51 & 2,975,000 & 2,956,416.531 \\
12.4 & 11.9 & 764 & 21 & 3814.40 & 3,023,000 & 2,971,082.065 \\
12.5 & 11.7 & 763 & 20 & 2800.38 & 2,237,240 & 2,228,516.481 \\
12.5 & 11.7 & 763 & 20 & 679.56 & 537,030 & 540,713.7864 \\
12.5 & 11.9 & 772 & 20 & 3776.23 & 3,010,000 & 3,004,085.459 \\ \hline \end{tabular}
\end{table} 
Table 1: Sample characteristics of wheat inventory data. SVR prediction model based on linear kernel function and the SVR prediction model based on Sigmoid kernel function were stable and smaller, while the mean square error of the SVR prediction model based on RBF kernel function fluctuated greatly and the average value was larger. Similarly, it can be seen from Figures 5 and 6 that the SVR prediction model based on the linear kernel function and the SVR prediction model based on the Sigmoid kernel function were both stable and closer to 1 than the SVR prediction model based on the RBF kernel function in terms of the evaluation of the determined coefficients. It can be seen that the SVR prediction model based on linear kernel function and the SVR prediction model based on Sigmoid kernel function were superior to the SVR prediction model based on RBF kernel function in the evaluation of the two indexes, and there was little difference between the SVR prediction model based on linear kernel function and the SVR prediction model based on Sigmoid kernel function in the rating convenience of these two indexes. The accuracy of wheat inventory prediction is a direct evaluation of different kernel function SVR prediction models. According to China's \"Food and Material Reserve Law Enforcement Supervision Work Regulations\", an error rate within the range of 3% is considered to be a no quantity problem. As shown in Figures 7 and 8, both the SVR  
Figure 4: Mean square error distribution of 20 testing prediction models under different kernel functions. Figure 3: Mean square error distribution of 20 training prediction models under different kernel functions.