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使用修改后的前向算法进行音频信号分析,用于土壤害虫检测的增强细分

Tusar Kanti Dash¹, Anurag Raj², Satyajit Mahapatra³

¹Electronics and Communication Engineering, C V Raman Global University, Bhubaneswar, 752054, India.

Scientific reports

|August 27, 2025

概括

这项研究引入了一种改进的基于音频的土壤害虫检测方法. 它使用先进的算法减少了20%的计算需求,并提高了5%的检测准确性.

科学领域:

农业科学
听力学
机器学习

背景情况:

土壤害虫每年造成农业的重大经济损失.
有效检测害虫对于作物健康,产量优化和可持续性至关重要.
非侵入性方法,特别是基于音频的检测,为传统的侵入性技术提供了低成本的替代方案.

研究的目的:

开发一种有效且准确的基于音频的土壤害虫检测系统.
减少处理害虫声音信号的计算开销.
通过新的算法修改,提高害虫检测的精度.

主要方法:

使用短时间能量功能进行信号细分的改进音频活动检测算法.
使用前进算法 (FFA) 实现数值稳定性和计算效率.
通过结合良性和损失函数的平方根平均值来改进FFA,以提高害虫检测.

主要成果:

与基线模型相比,音频活动检测算法平均减少了20%的计算需求.
修改后的FFA在病虫检测准确度上平均提高了5%.
拟议的方法在比较分析中显示出与几个基线模型相比的一致优异的性能.

结论:

开发的基于音频的害虫检测系统为土壤害虫识别提供了计算效率高和准确的解决方案.
整合短时间能量功能和修改后的FFA显著提高了病虫检测能力.
通过有效和经济的害虫防治策略,这种方法有助于实现可持续农业.

关键词:

没有.其他音频信号处理前向后向算法其他检测有害生物美国智能农业

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