# Research on Innovative Apple Grading Technology Driven by Intelligent Vision and Machine Learning
## 1 Introduction  
The challenges posed by manual apple sorting, such as fruit collisions, surface dents, low grading efficiency, human influence, and significant labor consumption, are receiving growing attention [1]. Automatic apple grading [2] technology has been increasingly and widely implemented in the apple industry, serving as a crucial component in both the industry and market distribution. In this context, research has increasingly focused onthe extraction of apple appearance features, non-destructive detection of the peel, and the design and optimization of fruit-grade classification models. With the significant advancements in computer technology in the agricultural sector in recent years, these technologies have substantially improved the efficiency of apple production and have played a crucial role in apple grading. In the field of quality grading for Fuji apples, machine vision-based grading technology has evolved from using a single feature to employing multi-feature fusion techniques, leading to the development of numerous advantageous algorithms. In the extraction of a single feature like apple diameter, for instance, [PERSON] [3] employed the CIELab color space to extract apple masks and calculated the number of apple pixels in the x and y directions using the corresponding binary images. They used the pixel count to represent apple size. [PERSON] [4] measured the maximum diameter pixels both horizontally and vertically on four different sides of the same apple and combined these eight values to determine the apple's diameter. [PERSON] [5] used a Laplacian operator to detect apple edges and locate the center point, ultimately calculating the maximum diameter of the apple. For the extraction of apple color features, [PERSON] [6] used the RGB color space to segment an apple from its background via the G/B and R/G ratios. They applied a super-red and super-green segmentation method to divide red areas and calculate their size for color grading. [PERSON] [7] converted apple images into the HIS color space, extracted the number of red pixels using the H component, and characterized the apples' color features by the ratio of red pixels from front and rear views. While using single features allows for quick apple grading, it cannot comprehensively reflect the quality grade of apples or meet diverse consumer demands. With further advancement in computer vision technology, researchers have gradually experimented with multi-feature grading of apples. [PERSON] [8] extracted color, texture, and shape features of defects, stems, calyves, and apple surface areas; these features were used to train decision tree support vector machines for classifying defects vs. non-defects. [PERSON] [9] utilized four images per apple to extract features like diameter, roundness, red area ratio, and defects, applying a weighted K-Means clustering algorithm for classification, achieving 93.3% accuracy. Employing multiple features in apple grading reduces misclassification and enhances the accuracy of the grading process. With the rapid development of deep learning, the application of non-destructive testing and intelligent sorting technologies in the apple grading process has become increasingly widespread. [PERSON] [10] combined color and diameter features to perform target detection and size grading on apple tree fruits, effectively identifying smaller targets and those with uneven lighting, achieving a grading accuracy of 90%. [PERSON] [11] proposed a novel multi-view spatial network for apple grading tasks, incorporating apple size information as one of the grading criteria. Their approach starts with a well-pretrained lightweight CNN to extract low-level features of apples. Subsequently, a spatial feature aggregation module is constructed using bidirectional LSTM and mean pooling to explore relevant apple information from multiple perspectives, showing promising application prospects for the multi-view spatial network in apple grading tasks.