# Combining Dielectric and Hyperspectral Data for Apple Core Browning Detection
## 5 Conclusions  
This study fully leveraged the advantages of hyperspectral imaging and dielectric technology, enhancing detection accuracy through data fusion and feature reduction. A method suitable for non-destructive detection of apple core rot disease was developed. Experimental results show that the multi-view optoelectronic fusion detection method (utilizing canonical correlation analysis and a support vector machine with a radial basis function) achieved a sensitivity of 99.98%, specificity of 99.70%, and accuracy of 99.70%, demonstrating reliable and accurate non-destructive detection of apple core rot disease.  
This method is not only applicable to non-destructive detection of apple core browning but also holds broad prospects in the agricultural field. Its potential for extension to the quality testing of other agricultural products marks a crucial step towards agricultural production intelligence, providing an innovative solution for improving product quality and  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Hidden Layer** & **Sensitivity \(\pm\) SD (\%)** & **Specificity \(\pm\) SD (\%)** & **Accuracy \(\pm\) SD (\%)** \\ \hline
512 & 100.00 \(\pm\) 0.00 & 90.48 \(\pm\) 3.92 & 89.28 \(\pm\) 4.74 \\ \hline
256–128 & 100.00 \(\pm\) 0.00 & 93.33 \(\pm\) 3.43 & 92.73 \(\pm\) 3.93 \\ \hline
128–64 & **100.00 \(\pm\) 0.00** & **94.17 \(\pm\) 2.69** & **93.74 \(\pm\) 3.03** \\ \hline
64–32 & 100.00 \(\pm\) 0.00 & 93.57 \(\pm\) 3.14 & 93.00 \(\pm\) 3.65 \\ \hline \hline \end{tabular} Note: Data are presented as mean \(\pm\) standard deviation (SD), with bold representing the optimal mean.  
\end{table}
Table 5: The impact of hidden layer results on detection performance.  
reducing agricultural losses. However, the experimental results have certain limitations, such as sample constraints and differences between laboratory conditions and real-world environments. Future research efforts could focus on expanding the sample range, considering environmental factors, optimizing data fusion methods, and developing real-time monitoring systems to enhance model applicability, robustness, and practicality. Integration into intelligent fruit quality management systems could further serve agriculture production and food safety.  
Conceptualization, H.L. and J.H.; methodology, H.L.; software, Y.S.; validation, Y.B., with support from J.H.; formal analysis, H.L.; investigation, H.L. and Y.S.; resources, Y.B.; writing--original draft preparation, H.L.; writing--review and editing, J.H.; supervision, J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under grant number 62366053, the Open Research Fund of Guangxi Key Lab of Human-machine Interaction and Intelligent Decision under grant number GXHID2204, and the \"Unveiling and Commanding\" Special Research Program of Yan'an University under grant number 2023 JBZR-021.  
Not applicable.  
The raw data supporting the conclusions of this article can be made available by the authors upon reasonable request.  
The authors would like to thank all the anonymous reviewers and editors for their valuable comments.  
The authors declare no conflicts of interest.