Artificial Intelligence and image processing technologies in combating olive fly

Abstract

The olive fruit fly (Bactrocera oleae) is one of the major pests causing significant economic losses in olive production worldwide, particularly in the Mediterranean region. Although traditional control methods rely on pesticide applications and pheromone traps, these approaches face substantial limitations in terms of environmental sustainability, cost-effectiveness, and consumer health. In recent years, advances in artificial intelligence (AI), machine learning, deep learning, and image processing technologies have offered innovative solutions for controlling B. oleae. This review evaluates the impact of drone- and camera-based systems, multispectral and hyperspectral imaging technologies, automated traps, and mobile applications on the monitoring, detection, and management of olive fruit fly populations. Examples of AI algorithms such as YOLO, CNN, Faster R-CNN, and SVM demonstrate considerable potential for real-time pest detection with high accuracy. Moreover, integrated pest management strategies utilizing geographic information systems (GIS), IoT-based sensors, and economic analyses have been shown to enhance efficiency while reducing pesticide use. In conclusion, AI-powered agricultural technologies provide significant contributions to the development of environmentally friendly, sustainable, and cost-effective strategies for combating the olive fruit fly.