# Detection of Defective Features in Cerasus Humilis Fruit Based on Hyperspectral Imaging Technology
## 1 Introduction  
_Census Humilis_ fruit is native to China, and is a popular healthcare fruit for the older generation. Because the fruit has a high calcium content and is easy to absorb, it is often called \"calcium fruit\" [1]. The _Census humilis_ fruit is not unique in flavor, but is rich in sugar, organic acids, protein, vitamins, total flavonoids, and other components. In addition to being eaten fresh for health reasons, it can also be used for deep processing product development (such as fruit vinegar and fruit juice); these products have important medicinal value for hyperlipidemia and cardiovascular and cerebrovascular systems. In recent years, with the continuous development of technology used to maintain freshness, the supply period of _Census humilis_ fruits has been extended, and the economic benefits have increased exponentially. This has significantly improved the added value after harvest and increased the income of farmers. However, the existence of external defects, such as rust spots, insect pest, and crack, has seriously affected the production and quality of _Census humilis_ fruits. These defects can easily cause fungi and infection of other intact fruits. The economic losses associated with these defects account for 1.0% of the industrial value of _Cerasus humilis_ fruit. Therefore, it is necessary to remove the defective fruit from the intact part. At present, the defective fruit is removed primarily through manual detection. This process is not only time-consuming but physically laborious and lacks efficiency. In particular, the sorting of naturally damaged fruit is more difficult. Therefore, there is an urgent need for a method that can quickly and accurately identify the defects of _Cerasus humilis_ fruit. Hyperspectral imaging (HSI) technology is a fusion technology of image and spectrum, which has the advantages of high sensitivity and fast measurement speed [2; 3; 4; 5]. It has become a rapid non-destructive detection and classification method. In recent years, this technology has been widely used in the detection of external features of fruit. [PERSON] et al. [6] used HSI technology and stoichiometry to identify bruised and normal tomatoes and built a multivariate analysis classifier model. The study showed that the established back propagation artificial neuron network (BP-ANN) model had the highest recognition rate, and the recognition rates of bruised and normal tomatoes were 89.29% and 100%, respectively. [PERSON] et al. [7] realized nondestructive testing of external quality indexes (intact, crack, rust spot, deformation, and insidious injury) of nectarines by using HSI technology combined with mapping information and the least squares support vector machine (LS-SVM) modeling method. The results indicate that the comprehensive identification accuracy of the model for external quality indexes of nectarine was 94.45%. [PERSON] et al. [8] used HSI technology combined with principal component analysis (PCA) to detect the hidden brusses of kiwifruit. It was concluded that that the detection error of this method for kiwifruit hidden brusses was 14.5%. [PERSON] et al. [9] collected reflectance images of four kinds of jujube fruits (intact, cracked, bruised, and insect damage) using HSI technology. The results showed that the SIMCA model had the best classification accuracy for intact, cracked, bruised, and insect pests, which reached 96%, 96%, 93.9%, and 95.6%, respectively. [PERSON] et al. [10] conducted a rapid identification study of intact and four damage classes of Lingwu jujube using hyperspectral imaging techniques (380-1030 nm). The results showed that the accuracy of the classification discrimination model (MSC-CARS-PLS-DA) for the calibration set and prediction set was 77.41% and 89.52%, respectively. The above research results show that it is feasible to detect fruit defects using HSI technology. However, from the current research situation, there are few reports on the detection and classification of the external defects of _Cerasus humilis_ fruits using HSI technology.