A Comprehensive Review of Pest Identification and Control in Smart Agriculture Using Scalable Quantum Convolutional Neural Networks and Wireless Sensor Networks

Abstract

Pest infestation remains a major challenge in agriculture, leading to significant crop losses and economic damage worldwide. The integration of smart agriculture technologies, particularly Wireless Sensor Networks (WSNs) and deep learning models, has enabled automated and efficient pest identification and control. This review presents a comprehensive analysis of recent advancements in pest detection using scalable Quantum Convolutional Neural Networks (QCNNs) and WSN-based monitoring systems. Conventional Convolutional Neural Networks (CNNs) have demonstrated high accuracy in pest detection by automatically extracting spatial features from image data, often achieving accuracy above 90% in classification tasks. Recent developments in quantum machine learning, particularly QCNNs, leverage quantum properties such as superposition and entanglement to enhance computational efficiency and learning capability, showing promising results in small-scale pest classification tasks. WSNs play a critical role in real-time environmental monitoring, enabling data collection from distributed sensors to support intelligent decision-making. The combination of QCNNs with WSNs enables scalable, real-time pest detection and targeted control strategies. However, challenges such as hardware limitations, data variability, and energy efficiency remain. This review highlights current methodologies, comparative analysis, and future research directions for sustainable and intelligent pest management systems.