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基于机器学习的果果虫活动分类使用声学传感器

Ivane Ann P Banlawe¹, Jennifer C Dela Cruz²

¹College of Engineering and Technology, Western Philippines University, Aborlan 5302, Philippines.

|November 25, 2023

概括

这项研究引入了一种新的非侵入性方法,用于使用音频分析和机器学习来检测果果虫 (MPW). 该研究在识别MPW活动方面取得了89.81%的准确性,为害虫管理提供了有前途的解决方案.

科学领域:

农业昆虫学农业昆虫学
声学信号处理声学信号处理
机器学习应用机器学习应用

背景情况:

果果粉虫 (MPW) 是一个重要的农业害虫,由于难以检测,造成了相当大的经济损失.
目前的MPW检测方法不足,因为这种害虫不会在果上留下外部物理损伤迹象.
在发现MPW之后,帕拉万岛在果出口方面处于隔离状态,这凸显了迫切需要有效的检测策略.

研究的目的:

使用音频特征提取和机器学习开发一种非侵入性方法来检测果纸虫 (MPW).
评估声学传感器用于识别MPW活动的有效性.
为未来在自动化MPW检测系统方面的进步建立一个基准.

主要方法:

使用 MATLAB 机器学习工具进行音频特征提取和分类.
评估了不同声学传感器的性能,并根据其最佳结果和可访问性选择MEMS传感器.
在隔音室内记录和分析MPW活动 (步行,休息,交配) 的声学数据.
用于特征提取的Mel频率切普斯特拉系数 (MFCC) 和用于分类器培训的支持矢量机 (SVM).

主要成果:

MEMS声学传感器在捕获MPW产生的声音方面表现出卓越的性能.
该研究成功地确定了成人MPW活动的独特声学特征,包括行走,休息和交配.
通过音频分析和机器学习实现了89.81%的整体准确度,通过音频分析和机器学习来表征果肉活动.

结论:

非侵入式的音频特征提取与机器学习相结合,为检测果纸虫 (MPW) 提供了一种可行的方法.
开发的声学检测系统显示了改善果种植中的害虫管理策略的巨大潜力.
这项研究为开发先进的自动化系统提供了基础,用于早期检测MPW,减少作物损失和贸易限制.

关键词:

在MEMS MEMS中使用.在SVM中,SVM是SVM.声学检测声学检测声学检测果纸质虫果纸质虫是一种虫.

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