# Research on Innovative Apple Grading Technology Driven by Intelligent Vision and Machine Learning
## 1 Introduction
[PERSON] [12] also proposed a real-time apple defect detection method based on the YOLO V4 deep learning algorithm. The input image is synthesized from three consecutive infrared images. They simplified the YOLO V4 network using channel and layer pruning methods to increase detection speed. Additionally, they introduced an L1 norm-based non-maximum suppression (NMS) method to eliminate redundant prediction boxes after fine-tuning the pruned network, further enhancing the model's detection speed. Previous work has partially addressed the task of apple grading, primarily focusing on characteristics like color and size. However, the feature of apple stems has not been adequately studied, despite the fact that national apple grading standards require premium, first-grade, and second-grade apples to have intact stems. This highlights the stem characteristic as a crucial indicator, underscoring the need for an efficient and swift stem detection algorithm to enhance apple grading tasks. Additionally, complex background information can interfere with the accurate extraction of apple features, necessitating the development of an effective algorithm for fruit segmentation. Building on prior research into automated apple classification and grading, this study uniquely integrates advanced computer vision techniques, image segmentation, and machine learning to create a comprehensive apple grading system. This system, aligned with apple grading standards, incorporates efficient and precise methods to evaluate the external quality of apples while avoiding secondary damage. The main contributions of this work are as follows:  
1. We introduce, for the first time, a stem indicator in the apple grading task. By leveraging computer vision and state-of-the-art model compression techniques, we propose a high-precision, lightweight stem detection algorithm called FDNet-p. Extensive experiments demonstrate its superior performance. In the apple grading process, FDNet-p is initially applied to filter out apples without stems. Additionally, we propose an auxiliary positioning strategy to prevent premature or delayed triggering of the camera capture. 2. During feature extraction, we propose an improved DPC-AWKNN segmentation algorithm and incorporate an adaptive brightness equalization strategy to enhance fruit body segmentation. Image processing techniques are then employed to extract color, diameter, and shape features of apples, aligning these with real-world manual grading standards. 3. Based on the multi-feature information of apples, we use various performance metrics to comprehensively evaluate machine learning classification algorithms. Ultimately, the GBDT algorithm is selected for implementing intelligent apple grading. The structure of the subsequent sections is as follows: In the Materials and Methods section, we first introduce the grading standards and dataset, then provide a detailed explanation of the principles of the FDNet-p stem detection algorithm, specific methods for apple feature extraction, and the GBDT apple grading model. In the Experiment and Result Analysis section, we compare and analyze the superiority of the FDNet-p and GBDT algorithms. The Discussion section outlines the limitations of this study, discussing potential future work. Finally, the Conclusions section summarizes our research and outlines future research directions.