Deep Learning and Optimization Approaches in Pest Identification and Control in Smart Agriculture Using Scalable Quantum Convolutional Neural Networks and Wireless Sensor Networks: A Review

Abstract

Pest infestation remains one of the major challenges in agriculture, causing significant crop losses and economic damage worldwide. The emergence of smart agriculture has enabled the integration of Wireless Sensor Networks (WSNs), Internet of Things (IoT), and artificial intelligence (AI) techniques to address these challenges. This review explores recent advancements in pest identification and control using deep learning and optimization approaches, with a focus on scalable Quantum Convolutional Neural Networks (QCNNs) integrated with WSNs. Traditional pest detection methods rely on manual inspection and conventional machine learning techniques, which are often time-consuming and inaccurate. Deep learning models, particularly Convolutional Neural Networks (CNNs), have demonstrated high accuracy in pest classification by automatically extracting features from image and sensor data. Recent studies show that hybrid models such as CNN-LSTM achieve up to 98.91% accuracy in pest detection, outperforming traditional methods . Furthermore, quantum machine learning techniques, including QCNNs, provide enhanced capability for processing high-dimensional agricultural data and improving prediction performance . The integration of WSNs enables real-time monitoring of environmental conditions, facilitating early pest detection and control. This review highlights key developments, compares existing approaches, and identifies future research directions for efficient and scalable pest management systems in smart agriculture.