# Grading Algorithm for Orah Sorting Line Based on Improved ShuffleNet V2
## 3 Results and Discussion
### Actual Grading Testing  
First, the classification speed of ShuffleNet_wogan on the testing platform was evaluated. Using Python's threading module, 10 multi-threaded tasks were created. Each batch loaded 10 Orah images for grading. The next batch was loaded only after all threads completed grading for the current batch. This design simulated the working conditions of a sorting line. It ensured that the grading time for each batch was determined by the longest grading time among the threads. A total of 1000 batches, or 10,000 images, was tested.  
The results show an average grading time of 51.44 ms per batch, with a maximum grading time of 87.02 ms. Thus, ShuffleNet_wogan is capable of handling 10 batches per second, equating to 10 Orahs per second on the conveyor chain.  
To evaluate classification accuracy, 200 Orahs from Grade A, Grade B, and Grade C were selected. These samples were mixed and input into the sorting line. The fruits were  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1** & **Parameters/10\({}^{6}\)** & **FLOPs/10\({}^{6}\)** \\ \hline ResNet50 & 0.9064 & 0.9045 & 0.9063 & 0.9054 & 23.52 & 4119.8 \\ DenseNet121 & 0.8970 & 0.8973 & 0.8970 & 0.8970 & 6.96 & 2881.6 \\ AlexNet & 0.8862 & 0.8861 & 0.8862 & 0.8861 & 57.02 & 711.5 \\ EfficientNet B0 & 0.9040 & 0.9028 & 0.9040 & 0.9031 & 4.01 & 400.4 \\ MobileNet V2 & 0.9034 & 0.9054 & 0.9033 & 0.9042 & 2.23 & 319.0 \\ MobileNet V3-Large & 0.9040 & 0.9030 & 0.9040 & 0.9034 & 4.21 & 227.0 \\ ShuffleNet V2\(\_\times\)1.0 & 0.8720 & 0.8733 & 0.8720 & 0.8726 & 1.26 & 149.6 \\ ShuffleNet\_wogan & 0.9112 & 0.9104 & 0.9112 & 0.9107 & 1.26 & 149.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Model comparison results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Grade** & **Mean/mm** & **Standard Deviation/mm** & **Maximum Value/mm** & **Minimum Value/mm** & **Pass Rate** \\ \hline Grade A & 0.06 & 1.10 & 2.06 & \(-\)1.76 & 98\% \\ Grade B & 0.21 & 1.15 & 2.00 & \(-\)1.79 & 99\% \\ Grade C & 0.0 & 1.17 & 2.07 & \(-\)1.83 & 98\% \\ \hline Total & 0.09 & 1.14 & 2.07 & \(-\)1.83 & 98.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Diameter measurement deviation results.  
directed to the corresponding output channels based on their classification, and the correct classification ratio was recorded at the output. The conveyor chain was set to transport 10 Orahs per second.  
The results shown in Figure 22 indicate that the time-sequential grade judgment algorithm significantly improved the recognition success rate. The sorting accuracy reached 97% for Grade A, 87.5% for Grade B, and 93% for Grade C. The overall sorting accuracy rate was 92.5%. From the output results of 10 classifications for each Orah, defects in appearance were highly likely to be correctly classified within the image range of five adjacent positions, effectively filtering out misclassifications occurring during single recognitions. The lower accuracy for Grade B is attributed to insufficiently distinct features compared to Grade C, leading to defective areas occasionally being misclassified into Grade C. This issue requires further improvement.