Fourier Transform Driven Bioacoustics for Frequency-Based Pest Deterrence in Sustainable Agriculture

Abstract

The focus of this research is on using bioacoustics for frequency-based pest deterrence in sustainable agriculture, with the Fourier transform as the driving force. The critical need for new and improved methods of pest control in agricultural settings is addressed. This study, which makes use of cutting-edge technology, investigates how the Fourier Transform might be used as a useful instrument in the fight against pests. This research makes use of a cutting-edge algorithm for pest control; it's based on Fourier Transform bioacoustic analysis. By using the "Insect Bioacoustic Signals (IBS) Dataset," this study reveals the algorithm's effectiveness in recognising and managing pests, as indicated by a remarkable classification accuracy of 93%. This study makes important contributions to the growing body of sustainable agriculture knowledge and has far-reaching consequences for the agricultural sector. A revolutionary new method of pest control is presented, with the potential to increase agricultural output and sustainability while decreasing crop losses. Specialised tools and libraries such as the Fast Fourier Transform (FFT) method from the NumPy toolkit for spectral analysis, Scikit-learn for machine learning approaches, and Librosa for audio signal processing, were used in this work to produce these encouraging outcomes. In conclusion, this research highlights the promise of bioacoustics based on the Fourier Transform to usher in a new era of environmentally responsible farming by effectively discouraging pests.