## 3 Analysis of Experimental Results
From the spectral data classification performance of leaves, the classification results of hyperspectral data of potato leaves using the proposed algorithm in this paper are more accurate than the other two deep learning models, and the multidimensional fusion Atrous-CNN with cavity convolution is used to train the loss function to fall faster and converge better during the training process. 1 DCNN uses convolutional operation for feature extraction, which can more effectively identify the deeper feature information of hyperspectral data than traditional machine  
Figure 10: Comparison of evaluation results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **K-Fold cross Validation** & **1 DCNN** & **Multinomial Naive Bayes Classifier** & **GBDT** & **SVM** \\ \hline \multirow{4}{*}{Train set} & The first time & 0.9979 & 0.6582 & 0.9707 & 0.9508 \\  & The second time & 0.9987 & 0.6583 & 0.9706 & 0.9509 \\  & The third time & 0.9989 & 0.6592 & 0.9708 & 0.9517 \\  & The fourth time & 0.9978 & 0.6581 & 0.9706 & 0.9522 \\  & The fifth time & 0.9982 & 0.6572 & 0.9706 & 0.9510 \\  & Average & 0.9983 & 0.6582 & 0.9707 & 0.9513 \\ \hline \multirow{4}{*}{Test set} & The first time & 0.9967 & 0.6577 & 0.9682 & 0.9527 \\  & The second time & 0.9980 & 0.6516 & 0.9735 & 0.9528 \\ \cline{1-1}  & The third time & 0.9990 & 0.6664 & 0.9718 & 0.9482 \\ \cline{1-1}  & The fourth time & 0.9976 & 0.6526 & 0.9711 & 0.9470 \\ \cline{1-1}  & The fifth time & 0.9997 & 0.6627 & 0.9688 & 0.9554 \\ \cline{1-1}  & Average & 0.9982 & 0.6582 & 0.9707 & 0.9512 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of evaluation results. learning methods. In the model's training, the difference between predicted and accurate labels is used to construct the loss function, the gradient descent method minimizes the loss function, and the optimal model is finally obtained through continuous training. From the five tests of k-weight cross-validation, we can see that the potato disease identification model trained by 1 DCNN has better accuracy than the three machine learning algorithms. It indicates that the deep learning network using convolutional operations is more effective for feature extraction of hyperspectral data and performs better in the task of spectral classification than the traditional machine learning methods using polynomial and kernel techniques. After k-fold cross-validation, this study re-divided the data set. The number of training sets was 47,352, including 11,782 pieces of spectral information of anthracnose leaves, 25,402 pieces of spectral information of leaf blight leaves, and 10,168 pieces of spectral information of early blight leaves information. The number of test sets is 15,784, including 3957 pieces of spectral information of anthracnose leaves, 8595 pieces of spectral information of leaf blight leaves, and 3232 pieces of spectral information of early blight leaves. Figure 11 shows the confusion matrix of the prediction results of the three diseases using the 1D-CNN network. The marked position is the number of disease categories correctly identified using the spectral information of the diseased leaves using the 1D-CNN. Table 6 shows the classification accuracy and recall rate of the three diseases calculated using the confusion matrix. The accuracy and recall rates of the three diseases in the training set using the 1D-CNN network are above 0.99. In the test set, the recognition accuracy rates of the three diseases were all above 0.98, among which the recognition accuracy rate of anthracnose reached 0.9987, and the recognition recall rates of the three disease test sets were all above 0.97, of which for the anthracnose and leaf blight, the recognition recall rate was above 0.99. In summary, using the 1D-CNN network and hyperspectral image technology to identify potato plant diseases is feasible. Figure 12 shows the detection results of potato leaves with three anthracnose diseases, leaf blight, and early blight, using multidimensional fusion Atrous-CNN.