## 1 Introduction  
The apple, a vital element of China's fruit industry, offers a broad planting area and high total output [1]. Significant progress in automatic apple grading technology offers advantages in speed, efficiency, and human error reduction over traditional manual grading. Developing high-efficiency and stable automatic apple grading equipment necessitates high-performance, lightweight grading models, fostering the application of computer vision in apple grading [2]. This paper concentrates on recognizing and screening diseased apples to replicate manual grading scenarios, excluding substandard fruits, curtailing grading losses, and initiating lightweight recognition model research for diseased apples. Traditional target detection methods, such as sliding windows and manual feature extraction, are exemplified by techniques like Haar [3], HOG [4], Hu moment [5], SIFT [6], SURF [7], and DPM [8]. The evolution of computer vision and deep learning has ushered target detection into agricultural production prominence, with algorithms bifurcated into single-stage (e.g., YOLO series [9, 10, 11], SSD series [12, 13, 14], RetinaNet series [15, 16]) and two-stage detection algorithms (e.g., RCNN series [17], FasterRCNN series [18]). Apple target detection, melding computer vision and agriculture, automates apple identification and localization in images. [PERSON] et al. [19] refined the YOLOv4 detection algorithm for apple defect identification, employing channel and layer pruning strategies and an L1-paradigm non-extreme value suppression method to prune redundant detection frames,achieving a 93.9% average detection accuracy. [PERSON] et al. [20] integrated the Res2 Net module into the RetinaNet algorithm, coupling a weighted bi-directional feature pyramid network with a focus-based loss and efficient intersection and concatenation ratio in the joint loss function for apple target detection. [PERSON] et al. [21] implemented GhostNet in the improved YOLOv4 apple target detection task, reconstructing the feature extraction network with a depth-separable convolutional lightweight necking network and detection head, integrating coordinate attention into the feature pyramid, and reconfiguring the feature extraction network. While enhancing model detection accuracy, such advancements often escalate model complexity. Some lightweight apple target detection models depend on depth-separable methods, which, although reducing weight, forfeit global channel information, leading to a concomitant problem of accuracy loss [22]. At present, in most of the apple target detection tasks, the main research is on the detection of apple fruit, for the apple grading system in the fruit disease identification research is relatively small. At the same time, most of the detection tasks focus on improving the detection accuracy of the model, resulting in the complexity of the model becoming high. Some of the lightweight apple target detection models mainly use the depth of separation of lightweight method, and the depth of separation of convolution cuts off the cross-channel communication between the information. Due to the loss of global channel information, the backbone of the network extraction of the features of the information is not adequate. For the model to be lightweight, at the same time it faces the loss of accuracy. If the model is to be lightweight at the same time, the loss of precision is a problem. Aiming at the identified challenges, the authors of this paper, using the YOLOv8n algorithm, the structural re-parameterization technique, and efficient neck structure, designed a lightweight non-destructive detection of diseased fruits in apple grading, simulated the initial apple screening scenario of manual grading, and propose a lightweight, high-performance diseased apple recognition model. This model focuses on the recognition and screening of four apple appearance features: healthy, speckled, rotted, and scabbed.