Integrating Artificial Intelligence for Monitoring and Assessment of Field Biological Control Strategies: Integrasi Kecerdasan Buatan untuk Pemantauan dan Evaluasi Strategi Pengendalian Hayati di Lapangan - Awaal Mahmood Lafta (Universitas Muhammadiyah Sidoarjo, 2026)
General Background Biological control is a core component of integrated pest management aimed at reducing chemical pesticide dependence while preserving ecosystem integrity. Specific Background However, conventional monitoring and evaluation of biological control programs remain labor-intensive, time-consuming, and limited by declining taxonomic expertise. Knowledge Gap There is still limited evidence from real-field conditions on how artificial intelligence, sensor technologies, and unmanned aerial vehicles can be integrated into a unified monitoring, prediction, and deployment framework for biological control. Aims This study reviews and evaluates the integration of AI-based monitoring systems, predictive modeling, and drone-assisted deployment of biological control agents in agricultural fields. Results Field trials in Central California and Southern India demonstrated high pest detection accuracy (90–95%), reliable predictive performance (AUC > 0.89), improved deployment efficiency, and pest suppression ranging from 55% to 78% across different agents. Novelty The study presents a comprehensive, field-tested framework combining computer vision, acoustic sensing, hyperspectral imaging, and UAV-based release within a single operational system. Implications The findings indicate that AI-supported biological control offers a scalable and cost-efficient pathway toward proactive, environmentally responsible pest management across diverse cropping systems. Keywords: Artificial Intelligence, Biological Control, Unmanned Aerial Vehicles, Pest Monitoring, Integrated Pest Management Key Findings Highlights: Multi-sensor AI systems achieved consistently high field-level pest detection accuracy. Drone-based release reduced labor costs while improving spatial coverage. Predictive modeling enabled earlier and more targeted biological interventions.