VQ-Rice: Integrating Variational Quantum Models for Intelligent Rice Disease Classification

This study presents a novel hybrid quantum-classical framework for rice disease diagnosis, leveraging variational quantum circuits (VQCs) to address the limitations of traditional and deep learning models in precision agriculture. The proposed Quantum Variational Rice Disease Network (QVRDN) integrates quantum feature encoding, variational quantum processing, and adaptive optimization to achieve superior classification accuracy, efficiency, and robustness. Using a curated dataset of 3000 annotated rice leaf images spanning major disease categories, the QVRDN framework applies dimensionality reduction and quantum angle encoding to transform the image features into quantum states, which are then processed by parameterized quantum circuits for disease classification. Experimental results demonstrate that QVRDN outperforms classical models, including SVM, random forest, CNN, and ResNet50-achieving, the highest accuracy of 97.8%, faster inference times, and greater resilience to noise and limited data. The compact design of the framework enables edge deployment without GPU dependency, making it suitable for resource-constrained agricultural environments. By demonstrating the feasibility and advantages of quantum machine learning in crop health monitoring, this study establishes a foundation for quantum-enhanced, data-efficient agricultural diagnostics and paves the way for future advances in intelligent, field-ready quantum geoinformatics systems.