Wireless Sensor-Driven Smart Agriculture Using Scalable Quantum Convolutional Models

Abstract

Smart agriculture has emerged as a transformative approach to enhance crop productivity, resource efficiency, and environmental sustainability using advanced sensing and computational technologies. Wireless Sensor Networks (WSNs) enable real-time monitoring of agricultural parameters such as soil moisture, temperature, humidity, and crop health. However, traditional machine learning models struggle to efficiently process high-dimensional, noisy, and heterogeneous agricultural sensor data. This study proposes a Wireless Sensor-Driven Smart Agriculture framework using Scalable Quantum Convolutional Models (SQCM). The proposed model integrates wireless sensor data acquisition with quantum-inspired convolutional neural networks to enhance pattern recognition, predictive accuracy, and scalability. The quantum convolutional layers enable high-dimensional feature representation, while classical layers ensure efficient decision-making for agricultural optimization. The model is evaluated using agricultural sensor datasets, and performance is measured using accuracy, precision, recall, and RMSE for environmental prediction tasks. Experimental results demonstrate that the proposed quantum-enhanced architecture outperforms traditional machine learning and deep learning models in crop condition prediction and resource optimization. The framework is suitable for precision agriculture, automated irrigation systems, and smart farming applications.