## 3 Analysis of Experimental Results
Regarding the recognition accuracy of total pixels, the recognition accuracy using the multidimensional \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Assessment Metrics**} & \multicolumn{3}{c}{**Models**} \\ \cline{3-5}  & & **3D-** & **Multidimensional** & **Multidimensional** \\  & & **CNN** & **Fusion CNN** & **Fusion Atrous-CNN** \\ \hline \multirow{4}{*}{Train} & \multicolumn{3}{c}{**Train-100**} \\  & Time-100 & 2:15:50 & 2:02:11 & 207:57 \\  & Loss-100 & 0.0259 & 0.0201 & 0.0141 \\  & Precision-100 & 98.92\% & 99.16\% & 99.41\% \\ \multirow{2}{*}{Val} & Loss-100 & 0.0231 & 0.0336 & 0.0233 \\  & Precision-100 & 99.07\% & 98.44\% & 99.13\% \\ \hline \multirow{4}{*}{Train} & \multicolumn{3}{c}{**Train-500**} \\  & Time-500 & 11:42:40 & 10:52:13 & 10:59:43 \\ \cline{1-1}  & Loss-500 & 0.0195 & 0.0106 & 0.0054 \\ \cline{1-1}  & Precision-500 & 99.18\% & 99.57\% & 99.78\% \\ \cline{1-1} \cline{2-5} \multirow{2}{*}{Val} & Loss-500 & 0.0214 & 0.0254 & 0.0226 \\ \cline{1-1}  & Precision-500 & 99.16\% & 98.86\% & 99.31\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Training results of three network models. Figure 8: Loss function and accuracy of three network model training. fusion Atrous-CNN model improved by 0.51% compared with the 3D-CNN method and by 0.94% compared with the multidimensional fusion CNN method. In order to evaluate the performance of the model independently of the data set, this study uses the k-fold cross-validation method to divide the hyperspectral data of the diseased pixels five times, that is, the k-fold cross-validation species k = 5, and the number of training sets for each division is 50,508. The number of test sets is 12,627. The data division is shown in Figure 9. This study trained the five-times-divided data with 1 DCNN, SVM, gradient_boosting_model, and multinomial naive Bayesian classification. The evaluation results are shown in Table 5 and Figure 10. The average accuracy of k-fold cross-validation of 1 DCNN. Compared with the polynomial naive Bayesian model, the accuracy rate increased by 0.3401. Compared with the gradient_boosting_model model, the accuracy rate increased by 0.0276 and the average accuracy rate of SVN increased by 0.047. The proposed multidimensional fusion Atrous-CNN fuses 3D convolution with 2D-AtrousCNN and 1D-AtrousCNN, which can not only reduce the training parameters of the network but also ensure the model's effective spatial feature extraction of hyperspectral data compared with the training of the classification task by processing hyperspectral data features entirely through the use of 3D convolution operation. The ability to extract the spatial characteristics of hyperspectral data is also ensured. Compared with the traditional \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Category Labels**} & \multicolumn{5}{c}{**Models**} \\ \cline{2-7}  & \multicolumn{2}{c}{**3D-CNN**} & \multicolumn{2}{c}{**Multidimensional Fusion CNN**} & \multicolumn{2}{c}{**Multidimensional Fusion Atrous-CNN**} \\  & **Correct Pixels** & **Precision** & **Correct Pixels** & **Precision** & **Correct Pixels** & **Precision** \\ \hline Healthy leaf pixels (16,970) & 16,894 & 99.55\% & 16,499 & 97.22\% & 16,934 & 99.79\% \\ Diseased leaves pixels (3173) & 2940 & 92.66\% & 3111 & 98.05\% & 3165 & 99.75\% \\ Background pixels (29,773) & 29,756 & 99.94\% & 29,752 & 99.93\% & 29,758 & 99.95\% \\ Whiteboard pixels (2513) & 2510 & 99.88\% & 2509 & 99.84\% & 2511 & 99.92\% \\ Total (52,429) & 52,100 & 99.37\% & 51871 & 98.94\% & 52,368 & 99.88\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Test results of three network models. Figure 9: Data partition diagram. 1D and 2D convolution pooling for feature extraction, the null convolution operation provides no loss of data information. It significantly increases the perceptual field of convolution calculation, which ensures the feature extraction capability of the model.