# Efficient Non-Destructive Detection for External Defects of Kiwifruit
## 4 Conclusions  
In this study, a non-destructive detection method for external defects of kiwifruit based on ResNet was proposed. The experiment firstly optimized the learning rate value,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**P/\%** & **R/\%** & **S/\%** & **F1 score/\%** \\ \hline
100.0 & 99.3 & 100.0 & 99.6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Model evaluation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **True** & **False** & **Time/ms** & **Acc/\%** \\ \hline AlexNet & 431 & 36 & 31.0 & 92.3 \\ VGG16 & 423 & 44 & 126.0 & 88.4 \\ InceptionV3 & 416 & 51 & 40.0 & 89.1 \\ ResNet34 & 446 & 21 & 43.0 & 95.5 \\ ResNet34 + CBAM & 465 & 2 & 46.0 & 99.6 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the results of the five models.  
attenuated the learning rate through a cosine-simulated annealing attenuation strategy, and used the Adam algorithm to accelerate the model convergence. Secondly, the optimal fusion method of ResNet34 with CBAM was investigated. The accuracy of ResNet34 + CBAM and ResNet34 in relation to the validation set was compared, and it was found that ResNet34 + CBAM has better stability than ResNet34. Finally, validation in relation to the test set reveals that the overall recognition accuracy of the ResNet34 + CBAM is 99.6%, which is higher than 99.0% for all evaluation metrics. Each index also verifies that ResNet34 + CBAM is capable and stable in recognizing kiwifruit. Compared with AlexNet, VGG16, InceptionV3, and ResNet34, it is found that within the online sorting time allowance, ResNet34 + CBAM improves the recognition accuracy over AlexNet, VGG16, InceptionV3, and ResNet34 by 7.9%, 12.7%, 11.8%, and 4.3%. Therefore, the method involved in this study provides technical support for online real-time detection and sorting of kiwifruit.  
In this study, we achieved highly accurate detection of external defects in kiwifruit, but there exists the following work for further investigation.  
The problem of model memory occupation is not taken into account. In the actual grading scenario, the hardware devices are not able to meet the running computation amount. Therefore, the subsequent work needs to ensure that the accuracy does not fall under the premise of reducing the number of operational parameters and computational to achieve model's light weight. The main external presence defects of two varieties of kiwifruit were selected for this experiment. We need to further expand the dataset of defects and varieties with a small number of samples or periodic defects.  
Conceptualization, F.W.; methodology, F.W.; software, Y.P.; validation, C.L. and B.Z.; formal analysis, L.Z.; investigation, F.W.; resources, C.L. and B.Z.; data curation, F.W. and Y.P.; writing--original draft preparation, F.W.; writing--review and editing, C.L.; visualization, L.Z.; supervision, C.L. and B.Z.; project administration, F.W. and Y.P.; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Beijing Nova Program (No. 20220484066).  
Not applicable.  
We thank [PERSON] for his valuable advice in method conceptualization, result validation, and manuscript writing.  
The authors declare no conflict of interest.