Bioacoustic Monitoring and AI Applications in Insect Pest Management

Abstract

Effective monitoring of insect populations is essential for sustainable pest management and for supporting Integrated Pest Management (IPM) strategies that reduce reliance on chemical pesticides. Bioacoustic methods have recently emerged as a promising approach for monitoring insects by analyzing the sounds and vibrations they produce during activities such as feeding, movement, and communication. This review examines the application of bioacoustics in insect monitoring and pest management, with particular emphasis on recent advances in artificial intelligence (AI) and automated detection technologies. The biological foundations of insect sound production, acoustic monitoring technologies, and AI-based analytical methods are discussed. Machine learning and deep learning models enable automated detection and classification of insect species based on acoustic signals, facilitating early pest detection and biodiversity monitoring. Bioacoustics has been applied to detect and identify insect pests, monitor stored-product insects, and manipulate insect behavior using acoustic and vibrational signals. Despite these advances, challenges remain, including environmental noise interference, limited acoustic datasets, and technical constraints of monitoring systems. Future research should focus on improving datasets, signal processing methods, and the integration of bioacoustics monitoring with precision agriculture and IPM frameworks to support sustainable crop protection.