## 1 Introduction
Since disease identification based on hyperspectral images belongs to the field of pattern recognition, sparse representation combined with dictionary learning algorithms has been widely used in face recognition [13], image classification [14], and other fields. [PERSON] et al. [15] established an HSI denoising algorithm by applying dictionary learning and sparse coding theory, extended into the spectral domain, and their experiments illustrated that the denoising result obtained using the proposed algorithm is at least 1 dB better than those of the comparison algorithms. [PERSON] et al. [16] proposed two multitemporal dictionary learning algorithms, expanding on their K-SVD and Bayesian counterparts, and the results were effective. Therefore, how to effectively apply the sparse representation and dictionary learning methods to identify the diseased area is the focus of our study. The objective of this work was to develop a method for identifying different nectarine diseases rapidly and nondestructively. \"Zhongyou NO.9\" variety nectarines with fruit cracking and rust spots were taken as study objects, and visible hyperspectral images of the two diseases were collected using a hyperspectral imaging system. Firstly, addressing the problems of the high dimensionality and linear inseparability of hyperspectral image data, a recognition model for common nectarine diseases based on dictionary learning and sparse representation was studied. Secondly, as feature extraction is the most important and difficult part in pattern recognition [17]; moreover, there were significant color differences among the diseased, normal, and background parts. According to the influence of different color features on the recognition results we can obtain an optimal feature vector so as to enhance the reliability and robustness of the model. Finally, in the process of dictionary learning, as the recognition area included three categories, diseased, normal and background, an improved LK-SVD algorithm was proposed that integrated the category label into the dictionary learning process, and then a discriminant over-complete feature dictionary was obtained for the sparse reconstruction of the original data to obtain the classification categories. This study will provide a basis for nondestructive and on-line identification in nectarine and other agricultural products. The core contributions of this paper can be summarized as follows. We deeply analyze the color features in the nectarine hyperspectral images according to the color characteristics of the nectarine itself and the significant color differences that exist in the three categories of nectarine (diseased, normal, and background parts). We propose an improved LK-SVD dictionary learning method that differs from the traditional machine learning method. Multiple color features combined with the improved LK-SVD are proposed. Through dictionary optimization, a dictionary with a strong category discrimination ability and corresponding linear classifiers can be obtained, which optimizes the algorithm while reducing the amount of computation. Applications for disease identification in nectarine using hyperspectral imaging are lacking. As the research object of this study is disease identification, samples with fruit cracking and rust spots were selected for the experiment, and then, the samples were arranged based on the hyperspectral-image-acquisition system for hyperspectral data collection.