###### Abstract  
As one of the world's most crucial crops, the potato is an essential source of nutrition for human activities. However, several diseases pose a severe threat to the yield and quality of potatoes. Timely and accurate detection and identification of potato diseases are of great importance. Hyperspectral imaging has emerged as an essential tool that provides rich spectral and spatial distribution information and has been widely used in potato disease detection and identification. Nevertheless, the accuracy of prediction is often low when processing hyperspectral data using a one-dimensional convolutional neural network (1D-CNN). Additionally, conventional three-dimensional convolutional neural networks (3D-CNN) often require high hardware consumption while processing hyperspectral data. In this paper, we propose an Atrous-CNN network structure that fuses multiple dimensions to address these problems. The proposed structure combines the spectral information extracted by 1D-CNN, the spatial information extracted by 2D-CNN, and the spatial spectrum information extracted by 3D-CNN. To enhance the perceptual field of the convolution kernel and reduce the loss of hyperspectral data, null convolution is utilized in 1D-CNN and 2D-CNN to extract data features. We tested the proposed structure on three real-world potato diseases and achieved recognition accuracy of up to 0.9987. The algorithm presented in this paper effectively extracts hyperspectral data feature information using three different dimensional CNNs, leading to higher recognition accuracy and reduced hardware consumption. Therefore, it is feasible to use the 1D-CNN network and hyperspectral image technology for potato plant disease identification. hyperspectral data of potatoes; atrous convolution; deep learning; multidimensional fusion Atrous-CNN +
Footnote †: journal: Medical  
management, and prevention, the most popular approach is to combine machine learning and image classification methods with multiple imaging techniques for disease detection of plants [18; 19; 20; 21]. However, traditional image-based classification methods cannot identify diseases that are difficult to detect beyond RGB images because they only consider image information and lack depth data features [22]. Hyperspectral imaging has emerged as a crucial technique in recent years, providing valuable spectral and spatial information for potato disease detection and identification [23; 24; 25]. The combination of hyperspectral image techniques, preprocessing methods, and deep learning convolutional neural networks has proved effective in detecting potato late blight [26]. Other researchers have used multispectral image systems to detect plant growth in a noninvasive manner [27], while the Cube CNN SVM (CCS) method has been shown to improve spectral image classification by extracting high-level features directly from raw data [28]. Previous studies have also shown that 3D-CNN can achieve better classification accuracy than 2D-CNN without preprocessing [29]. Multiscale wavelets, combined with in-depth feature information extracted by 3D-CNN, can generate super-resolution hyperspectral images from low-resolution ones [30]. However, initial 3D-CNN networks tend to suffer from overfitting and higher training costs, necessitating more hardware resources and training time, resulting in poor generalization of the overall network model [31]. To address these issues, a combined 2D-3D model approach can extract both spatial and spectral features, resulting in better fusion features for hyperspectral image classification (HSIC) [32], while reducing the network structure parameters [33]. Computer vision application in agriculture has become an alternative solution to manual detection [34]. [PERSON] et al. designed a hyperspectral line scan device for virus damage detection in different potatoes [35]. They demonstrated that a deep learning approach improved the accuracy of real-world potato disease detection.