## 3 Analysis of Experimental Results  
For the method part, we use the dilated convolution layer instead of the conventional convolution pooling operation to solve the data loss problem in information extraction. We compare the standard convolution with the dilated convolution, as shown in Figure 7. By comparing the experimental results, using the dilated convolution layer can improve the efficiency of data feature extraction and increase the convolutional computational field while maintaining information integrity. To better validate the detection performance of the proposed algorithm, the traditional 3D-CNN and multidimensional fusion CNN and multidimensional fusion Atrous-CNN are compared in training experiments. The total data volume is 262,144 (512 x 512), with 209,715 data in training set accounting for 80% of the total data and 52,429 data in the validation set accounting for 20% of the total data. The hardware environment is an Intel Xeon E5-2650 v4 processor, NVIDIA Tesla V100-PCIE-16 GB graphics card, and 256G RAM. Figure 8 shows the training process of hyperspectral data disease detection of potato leaves using three network models. The training results show that the loss function of the training process using the proposed multidimensional Atrous-CNN model decreases faster and converges better than the other two network models. Furthermore, the accuracy of prediction using the multidimensional Atrous-CNN model is also significantly higher than the other two network models. The training performance of this method outperformed the other two models in both 100 and 500 training sessions. Table 3 shows the comparative training results for classifying potato leaf hyperspectral image data using three network models: 3D-CNN, multidimensional fusion CNN, and multidimensional fusion Atrous-CNN. According to the training results, the training time of the 3D-CNN model is longer than that of the multidimensional CNN structure at 100 training times, the prediction accuracy of feature extraction using null convolution is higher than that of the traditional convolution-pooling operation, and the accuracy of the proposed multidimensional fusion Atrous-CNN model is improved by 0.69% over the multidimensional fusion CNN model in the validation set at 100 training times. The accuracy of the proposed multidimensional fusion Atrous-CNN model is improved by 0.69% compared to the multidimensional fusion CNN model in the validation set at 100 training cycles. The training time is significantly reduced compared to the 3D-CNN network. At 500 training cycles, the accuracy of all three network models for potato plant leaf disease classification improved with increasing training times on the training set performance. Among them, the accuracy of the training set using the multidimensional fusion Atrous-CNN method was as high as 99.78% after the 500 th training. The accuracy of this method on the training set was improved by 0.6% compared to the 500 th-training 3D-CNN method and by 0.21% compared to the multidimensional fusion CNN. In the validation set, the accuracy of this method improves by 0.15% compared to the training 500 times 3D-CNN method and 0.45% compared to the multidimensional fusion CNN method. Figure 7: Standard Convolution and Hole Convolution. Table 4 shows the results of disease detection using three network models for potato hyperspectral data. The potato hyperspectral data included four types of hyperspectral images: normal leaf pixels, diseased leaf pixels, background pixels, and whiteboard pixels. The results showed the highest prediction accuracy of the four types of pixels using the multidimensional fusion Atrous-CNN model. The recognition accuracy of all types of pixels reached more than 99.7%. Among them, in recognizing diseased leaf pixels, the accuracy was improved by 7.09% compared with the 3D-CNN method and 1.7% compared with the multidimensional fusion CNN method. The results proved that recognizing diseased leaves using multidimensional fusion Atrous-CNN has high effectiveness.