## 6 Conclusions  
One of the basic aims of this study is to analyze and process the audio signals of several typical trunk borers so as to achieve the purpose of detecting trunk borers. However, the process of signal acquisition is greatly affected by background environmental factors. In practical application, sound monitoring of trunk borers is often not guaranteed to be carried out in an interference-free environment. In order to solve this problem, work has been carried out to explore the feasibility of applying these advanced technologies in related fields, such as acoustic detection and sound classification, which are less affected by environmental factors, to this study. Finally, we found that the PNCC method is generally applicable to this study. In view of the fact that the design of this method is close to the characteristics of human speech, we think that it should be improved, in order to have a better ability to detect dry borer pests. Therefore, the work of improving the PNCC method to make it suitable for audio detection of trunk borer is carried out. In this work, firstly, the signal of trunk borers is briefly analyzed and, combined with the signal properties, an improved method based on the dmey wavelet system is established. The complete detection method includes two parts: feature extraction and classification. The performance of the classifier determines the detection accuracy. How to select and improve the performance of the classifier is another main work of this research. In this part, we compare the advantages and disadvantages of the commonly used methods to determine SVM as the classifier, and use GA to optimize the SVM meta-parameters to obtain the best classification results. Finally, in order to verify the effectiveness of the proposed method, we carried out experiments. The experimental results show that the proposed method is always more effective than the traditional sound detection technique for trunk borer pests. This provides an idea regarding the detection of trunk borers in a bad acoustic environment. In the process of research, we find that the proposed improved PNCC method inherits the strong anti-interference ability of the traditional PNCC method to white noise. This feature can significantly reduce the cost of audio recording equipment for trunk borer pests. In fact, cheap audio recording equipment, combined with the improved PNCC method, can not only detect whether the target tree contains trunk borers, but can also be used to monitor trunk borers on a large scale. It should be noted that the method proposed in this study has only been proven to be effective for the four trunk pests mentioned above. There is no extensive verification of whether it is applicable to other research objects. This is also a problem that needs to be discussed in future work. In future research work, we will further improve the existing methods in the following directions. First of all, at the data level, more species of trunk borer will be considered to verify the effectiveness of the method. In addition, the current research on acoustic detection of trunk borers is mainly based on a single signal source. However, multi-source signal often provide more information than a one-source signal. Therefore, the detection method based on multi-source signals will be developed. Then, at the feature level, we mention the idea of improving PNCC based on more advanced signal processing methods, which is an important part of the following work. Finally, at the classifier level, the method should distinguish the species and behavior of trunk borers in adverse acoustic environments. Methodology, H.Z. ([PERSON]); validation, H.Z. ([PERSON]), Z.H. ; formal analysis, H.Z. ([PERSON]); resources, H.Z. ([PERSON]) and X.L. ; data curation, H.Z. ([PERSON]) and X.L. ; writing--original draft preparation, H.Z. ([PERSON]); writing--review and editing, H.Z. ([PERSON]) and Z.H. ; supervision, L.S. and D.Z. ; project administration, L.S. ; funding acquisition, H.Z. ([PERSON]).