## 1 Introduction  
With high nutritional value, nectarines contain a variety of amino acids that are essential for the human body. At the same time, they can enhance immunity, which has high medicinal value. Hence, using hyperspectral imaging technology to realize the nondestructive identification of nectarines is a pivotal step in the process of nectarine industrialization [1]. In the process of the picking, preservation, and storage of nectarines, they are vulnerable to pests, diseases, and microbial pollution, which can lead to a great decline in product quality. Fruit surface defects caused by diseases have always been an important factor affecting the quality and price of fresh fruits. Fruit cracking and rust spot diseases are the most common physiological diseases of nectarines during their growth, and they are prone to occur from their break stage. Fruit cracking is a disease in which the fruit is exposed due to vertical or horizontal cracking nearing maturity, and the cracked fruit decays easily and become acidic [2]. The main causes of fruit cracking include deformational flowers, strong radiation, high temperature and drought, improper use of plant growth regulators, and excessive pinching. Rust spot is a fungal disease in which a layer of rust spots will appear in the fruit surface, and then, it begins to lose water and shrink as it is infested by the pathogen, resulting in a large number of fruit drops and a large-scale reduction in the yield. The main causes of rust spots include improper cultivation, an unsuitable climate and environment, pesticide and harmful dust pollution, mechanical damage, and pest infestation [3]. These diseases can seriously reduce the yield or result in a loss of commercial value. Therefore, we focus on these two diseases for research, and disease identification in nectarines is of great significance to improve their quality and market competitiveness [4]. Recently, with abundant spatial and spectral information, hyperspectral imaging systems have been widely used in the quality assessment for agricultural products [5; 6]. The ripeness of strawberries has been estimated using a hyperspectral imaging system in field and laboratory conditions, which showed better performance than other methods [7]. [PERSON] et al. [8] proposed a novel hyperspectral image classification framework using the meta-learner technique to train multi-class and multi-size datasets by concatenating and training the hybrid and multi-size kernels of convolutional neural networks (CNNs). At present, several studies on the internal and external quality assessment of nectarines have been conducted. The RPI (ripening index) and IQI (internal quality index) [9] have been used to assess the internal physicochemical properties and sensory perception of two cultivars of nectarine using VIS-NIR (visible near-infrared) hyperspectral images, showing great potential for further monitoring the evolution of intact nectarine ripeness in industrial setups. Various dimensionality reduction methods [10] have been used to extract the feature vectors from the dielectric spectrum and near-infrared spectrum, which can systematically reflect the advantages and disadvantages of the two spectrums in the quality assessment of nectarines, and the correlation coefficient and root mean squared error (RMSE) of the prediction set were 0.887 and 0.782, respectively. Hyperspectral image technology [11] combined with CARS-ELM (competitive adaptive reweighted sampling, CARS; extreme learning machine, ELM) has been successfully applied to realize the variety identification for nectarines, and the correlation coefficient and RMSE of the prediction set were 0.942 and 0.205, respectively, which provides the technical basis for the internal quality assessment of fruits. Nevertheless, most of the above studies aimed at the assessment of internal quality and variety identification of nectarines, and studies on disease identification are still in the theoretical analysis phase [12], let alone identification based on hyperspectral image technology.