# Grading Algorithm for Orah Sorting Line Based on Improved ShuffleNet V2
## 4 Conclusions  
Deep learning for fruit grading is characterized by high efficiency and accuracy, effectively reducing labor costs. This study collected a high-quality dataset of Orah samples based on an actual prototype. The imaging environment was carefully controlled to minimize noise during model training, ensuring abundant and reliable samples. Data augmentation was employed to further expand the dataset, improving the model's generalization ability. The ShuffleNet V2 network architecture was enhanced by replacing the activation function and incorporating the ECA attention module. These changes led to performance improvements without introducing additional computational overhead. Furthermore, transfer learning was applied to further enhance performance. According to the results of multi-model comparison experiments, ablation studies, and heatmap visualizations, these improvements significantly boosted model performance. Compared with conventional morphology-based defect detection methods, the model demonstrates significant advantages. The proposed ShuffleNet_wogan network model achieved a recognition accuracy of 91.12% on the test platform, with recognition time for processing 10 images under 100 ms, reaching a throughput of 10 Orahs/100 images per second. Assuming each Orah weighs 80 g with a loading efficiency of 70%, a single conveyor chain can sort approximately 2000 kg of Orah per hour. With four conveyor chains integrated into the prototype, the sorting capacity can reach 8000 kg per hour, equivalent to the sorting speed of 30-40 skilled manual workers. Over two years of continuous improvement, the sorting line prototype  
Figure 22: Confusion matrix for Orah sorting line. and its algorithm cumulatively sorted more than 10,000 tons of Orah, demonstrating excellent practical value. Taking advantage of the continuous rotation of Orahs during image acquisition, a time-sequence-based grading algorithm and a multi-sampling-based diameter algorithm were proposed. These algorithms address issues related to the precision of existing acquisition equipment and the accuracy of deep learning models, thereby improving practical grading accuracy. Overall tests demonstrated that the appearance grading accuracy reached 92.5%, and the diameter recognition pass rate achieved 98.3%, showcasing high usability. The appearance grading was divided into three levels, providing finer segmentation compared to binary classification deep learning models and enabling greater economic value. By combining appearance grading and diameter data, the harvested Orahs can be categorized into multiple quality grades, meeting diverse market demands. This study has some limitations due to research constraints. First, the model was evaluated only on a single cultivar: Orah. Fruits of different cultivars may have different visual characteristics, which could affect the model's generalizability. Second, all images in the dataset were collected using the sorting line. The lighting and background conditions remained highly consistent. Therefore, the model's performance under varying environmental conditions has not been tested. Future work will focus on further expanding the dataset and adopting advanced technologies to optimize the model. During future dataset collection, more attention will be given to maintaining data balance. This is expected to improve the classification performance for each grade. The model will also be extended to different cultivars and varied environmental conditions to enhance its adaptability and generalization. In addition, deployment on embedded and mobile platforms will be explored to support real-time applications in resource-constrained environments. Conceptualization, Y.B. and H.L. ([PERSON]): methodology, Y.B. ; software, Y.B. and H.L. ([PERSON]); validation, Y.B., H.L. ([PERSON]) and H.L. ([PERSON]); formal analysis, H.L. ([PERSON]); investigation, H.L. ([PERSON]); resources, B.G.M. ; data curation, X.W. ; writing--original draft preparation, Y.B. and H.L. ([PERSON]); writing--review and editing, Y.B. and H.L.