# Efficient Non-Destructive Detection for External Defects of Kiwifruit
## 1 Introduction  
Kiwifruit is sweet and sour, rich in nutrients, and known as the \"king of fruits\" [1]. China's kiwifruit is the world's largest in terms of cultivated area and production, and it is also a major pillar of China's fruit industry [2]. Currently, kiwifruit quality sorting in China's major kiwifruit producing areas is mainly manual, resulting in inconsistent sorting indexes, low sorting efficiency, and weak kiwifruit competitiveness. Moreover, kiwifruit is highly susceptible to external damage during growth, harvesting, and transportation, including sunburn, bruises, and scratches. These external defects not only affect the commercialization value of kiwifruit but can also be transmitted to healthy kiwifruit, causing unwanted losses. Therefore, it is necessary to adopt new technology to accurately and efficiently detect and sort kiwifruit to enhance the commodity value of kiwifruit. The external qualities of kiwifruit are assessed through traditional image processing methods more commonly. [PERSON] (2009) et al. [3] used threshold segmentation to detect the size, shape, and color of kiwifruit, but they did not address the external defect content. [PERSON] (2012) et al. [4] selected YCbCr and HSV colors for the detection of damaged and scarred kiwifruit, respectively, with an accuracy of 88%. [PERSON] used a near-infrared light source to extract scratched kiwifruit. The function relationship between the intensity of the light source and the external defects was established to achieve the detection of kiwifruit defects [5]. [PERSON] first processed the images using morphology and then used a BP neural network to categorize the external defects of kiwifruit into four classes, with an accuracy of more than 91.3% [6]. To summarize, traditional image processing techniques are availablefor specific recognition, but such methods have low recognition accuracy and poor stability for different samples. CNN (convolutional neural network) [7] has been widely studied and applied in fruit quality detection and grading because of its adaptability, high robustness, and excellent feature expression ability compared to traditional methods [8]. [PERSON] (2020) et al. [9] improved the traditional CNN for apple defect detection by reducing the parameters and the number of connections of the model with an experimental accuracy of 92%. [PERSON] (2022) et al. [10] proposed a CNN-based citrus tracking algorithm, which realized the historical information tracking and defect detection of citrus, and the experimental results showed that the recognition accuracy of defective citrus was 92.8%. [PERSON] (2009) [11] categorized plumes into five grades (excellent, good with scars, second grade, second grade with scars, and rotten) and proposed an intelligent feedback cognitive algorithm based on ASCNNs and integrated RVFL (Random Vector Functional Link) classifiers, with an average recognition rate of 98.2%. [PERSON] (2020) et al. [12] proposed a deep learning model based on ResNet50 (Residual Neural Network 50) for tomato external defect detection, which achieved an average accuracy of 94.6%. In addition, CNN has also been applied in the quality detection of fruits, such as balsam pear [13], papaya [14], jujube [15], watermelon [16], and grape [17]. CNN not only improves the sample recognition accuracy but also expands the range of sample category detection compared to traditional methods. This method has less research in the direction of kiwifruit surface defect detection. [PERSON] (2021) et al. [18] optimized the original network of YOLOv5, and the optimized model achieved 94.7% accuracy in detecting external defects in kiwifruit. However, the experiment did not consider practical grading application scenarios and the model was not well adapted to some defective samples. With the development of CNN, scholars have been able to acquire more information and features in images by deepening the depth of the network.