###### Abstract
Hyperspectral imaging is a valuable tool for disease detection in various crops from different angles (tissue to canopy) [36]. [PERSON] et al. [37; 38] used hyperspectral remote sensing to detect disease in potato plants; they only used spectral information but not imaging sensors. [PERSON] et al. used a point-spectrum approach without considering spatial information [39]. [PERSON] et al. successfully detected late blight on potato leaves using hyperspectral imaging to improve disease recognition [40]. [PERSON] et al. [41] used SVM to classify spectral features of potato plants infected with PVY obtained with a handheld device with recognition accuracy close to 90%. [PERSON] et al. proposed a lightweight convolutional neural network model [42] that could identify potato leaves with three different diseases, reducing the number of parameters and improving accuracy. [PERSON] et al. proposed a novel end-to-end deep learning model (CropdocNet) [43] for accurate and automated late blight diagnosis based on UAV-based hyperspectral images with an average accuracy of 98.09% on the test dataset. [PERSON] et al. [44], based on high-resolution field-of-view images and deep learning algorithms to extract late blight spots from unstructured field environments, demonstrated that unbalanced weighting of lesion and background categories could improve segmentation performance. [PERSON] et al. [45] proposed a deep collaborative attention network (PLB-2D-3D-A) by combining 2D convolutional neural network (2D-CNN) and 3D-CNN with a deep collaborative attention network (PLB-2D-3D-A) for hyperspectral deep learning classification architecture for images, showing promising results for early detection of potato late blight by deep learning and near-end hyperspectral imaging. [PERSON] et al. [46] proposed a weakly supervised learning approach to identify potato plant diseases by extracting high-dimensional features through a hybrid attention mechanism. Although potato disease detection technology has advanced significantly, there remain some challenges that impede the accurate and rapid identification of diseases. One such obstacle is the variety of potato diseases, which often present with similar symptoms, making them difficult to differentiate. Additionally, the complexity of diseases, which can result from a range of factors such as genetic and environmental conditions, further exacerbates this issue. Moreover, while 3D convolutional neural networks are commonly used for processing hyperspectral data, they are known to have high hardware requirements, and the accuracy of 1D convolutional neural networks for hyperspectral data is often suboptimal. Furthermore, numerous factors such as light, noise, distortion, and color changes present further challenges to disease detection, underscoring the need for increased algorithmic robustness and repeatability. To address these issues, this paper proposes a novel network architecture that leverages 1D, 2D, and 3D convolutional neural networks [47] in a multidimensional fusion approach. The network uses dilated convolution [48; 49; 50] for feature extraction, which avoids data loss and increases the perceptual field compared to the conventional convolution-pooling layer in CNNs. The proposed convolutional operation in different dimensions takes full advantage of hyperspectral data's spectral and spatial information, reducing network parameters and improving the model's generalization and classification accuracy. The purpose of this paper is the following:  
(1) To address the current problems of potato diseases that can cause serious harm to human health and crop yield and economic losses, we use deep learning technology to provide a new solution for detecting potato diseases to ensure their product and healthy growth.