AI-Driven Acoustic Drones with Quantum Signal Processing for Pest Control
Sai Krishna Thota *
Department of Information Technology, University of the Cumberlands, Kentucky, USA.
*Author to whom correspondence should be addressed.
Abstract
Aim: In this work, we explored the use of drones fitted with sound emitters and guided by artificial intelligence (AI) together with quantum-inspired optimization as an alternative to chemical pest control.
Study Design: The approach was tested only in simulations that modelled maize, orchard, and rice farming systems.
Methodology: The pest groups considered included crop-eating birds such as crows and sparrows, as well as insects like caterpillars and locusts.
Place and Duration of Study: This study was conducted in a controlled environment over five months, from January to May 2024.
Results: The adaptive drone system showed 20–40% higher repellence rates compared with fixed ultrasonic devices. Quantum-inspired optimization reduced the number of steps needed to identify effective sound frequencies by about 40% compared with classical trial-and-error approaches. However, energy use was 15–20% higher because of drone movement and on-board computation.
Conclusion: These findings are preliminary and based only on controlled simulations. The system shows promise, but several limitations remain. The results have not been tested under real farm conditions, where weather, field size, and maintenance demands may strongly affect performance. The quantum component was modelled in a simplified way and not run on actual hardware. Costs and regulatory issues were also not considered. Field trials will be necessary to confirm how well the system performs under practical farming conditions, including different climates, crop scales, and economic settings.
Keywords: Drones, acoustic pest control, quantum signal processing, integrated pest management, AI agriculture