AI-Enabled Pest Monitoring and Control Through Hybrid Quantum Learning Systems

Abstract

Agricultural productivity is significantly affected by pest infestations, leading to substantial crop losses and economic challenges worldwide. Traditional pest monitoring and control methods often rely on manual inspection, periodic surveillance, and chemical pesticide applications, which may result in delayed detection, excessive pesticide usage, environmental degradation, and reduced crop quality. The increasing availability of artificial intelligence (AI), Internet of Things (IoT), smart sensors, and quantum computing technologies presents new opportunities for developing intelligent pest management systems capable of real-time monitoring and adaptive decision-making. This study proposes an AI-Enabled Pest Monitoring and Control Framework using Hybrid Quantum Learning Systems (AIPMC-HQLS) for intelligent agricultural pest detection, classification, prediction, and control. The proposed framework integrates IoT-based environmental sensing, drone-assisted image acquisition, deep learning-based pest identification, and hybrid quantum learning mechanisms for optimized pest control recommendations. Convolutional Neural Networks (CNNs) are employed to extract visual pest features from crop images, while quantum-inspired optimization algorithms enhance classification accuracy and decision-making efficiency. Environmental variables including temperature, humidity, soil moisture, and crop health indicators are simultaneously analyzed to predict pest outbreaks and recommend preventive actions.