Grasshopper sound acoustic signal analysis using FFT and Butterworth filter

Abstract

Grasshoppers are among the most destructive agricultural pests, making early detection essential to reduce crop losses while limiting excessive pesticide use. Acoustic monitoring provides a non-invasive and environmentally friendly approach for pest detection; however, its effectiveness is often constrained by strong environmental noise in open field conditions. This study proposes a structured acoustic signal analysis framework for grasshopper detection based on fast fourier transform (FFT) and Butterworth bandpass filtering. Grasshopper sound recordings were collected in rice field environments and pre-processed using Butterworth filters with empirically determined cutoff frequencies to suppress out-of band noise. FFT was applied to extract dominant spectral features, and signal quality was evaluated using both direct signal-to-noise ratio (SNR) and power spectral density (PSD)-based SNR estimated via the Welch method. Results indicate that grasshopper acoustic energy is consistently concentrated within the frequency range of approximately 5.8–9 kHz. Although direct time-domain SNR slightly decreases after filtering due to attenuation of out-of-band components, PSD-based SNR improves significantly, reaching 25–28 dB, demonstrating effective spectral concentration and noise suppression. The proposed approach is computationally efficient, interpretable, and suitable as a foundational module for low-cost, real-time acoustic pest detection systems in precision agriculture.