Abstract
The sound-detection method of trunk borer is a very promising method in the field of forestry prevention and control of trunk borers. However, the detection accuracy of commonly used algorithms often decreases sharply in the case of noise reverberation interference. In practical applications, the sound monitoring of trunk borers often takes place in a harsh acoustic environment. To solve this problem, we intend to introduce methods which are effective in other related acoustic fields. Unfortunately, most of the methods are not suitable for acoustic detection of trunk borers and perform extremely poorly. After trying various methods, we found that Power-Normalized Cepstral Coefficients (PNCC) performed well in some cases, while it did not in others. This is due to the difference between speech and trunk borer sound. Therefore, an improved anti-noise PNCC based on wavelet package is proposed. The dmey wavlet system always obtains the best performance. We collected the audio of the following five dry borer pests for testing. They are red palm weevil, mountain pine beetle, red necked longicorn, Asian longhorn beetle and citrus longhorn beetle. In the experimental part, we used genetic algorithm-support vector machine (GA-SVM) as a classifier to compare Mel Cepstral Coefficients (MFCC), which are the most common methods in the field of audio detection of trunk borer, PNCC and improved PNCC in a variety of noise environments. The results showed that, compared with other methods, the newly proposed method can often achieve better results. The above experiments take the audio clips made of clear pest sound mixed noise. In order to further verify the effectiveness of the method, we designed another experiment with a harsh outdoor acoustic environment. We found that the proposed method achieved 88% accuracy and the traditional PNCC achieved 78% accuracy. However, the Mel cepstrum coefficient completely lost its ability to distinguish. In sum, the proposed PNCC based on wavelet packet decomposition can be used as a detection method for trunk borer in the harsh acoustic environment. This method has many advantages, including simple extraction and strong robustness to noise. Combined with cheap audio acquisition equipment, this method can effectively improve the early warning ability of forestry borer pests.
Highlights
Trunk borer mainly refers to all kinds of longicorn beetles, gibberries, weevil beetles, bark beetles, Lepidoptera and wood beetles
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
We found that the Power Normalized Cepstrum Coefficient (PNCC) method is generally applicable to this study
Summary
Trunk borer mainly refers to all kinds of longicorn beetles, gibberries, weevil beetles, bark beetles, Lepidoptera and wood beetles. Among the forest diseases caused by pests, trunk borer has become the most difficult to control in China because of its hidden living habits and the slow performance of damaged trees. Trunk borer has caused serious harm to the Chinese forest industry. The forest areas of southern and eastern Heilongjiang, Jilin and eastern Liaoning are infected by Chilo suppressalis. In some areas of Southwest China, Dendroctonus can cause disaster. The eight-toothed bark beetle destroyed trees in southeastern Inner Mongolia and eastern Tibet. The Huashan pine bark beetle lives in southern Gansu. In the eastern part of Inner Mongolia, the Hexi region of Gansu Province and Guanzhong Plain of Shanxi Province, the harm of stem borers such as Anoplophora glabripennis continues to worsen [1]
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