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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\u2010achieving, 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\u2010constrained agricultural environments. 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