Hybrid Quantum Neural Networks for Rice Plant Disease Classification Based on Leaf Image Analysis

Abstract

Classifying rice plant disease from leaf images presents a significant challenge due to the high visual similarity between different disease types, which can limit the effectiveness of purely classical deep learning models. This research addresses this problem by developing and evaluating a hybrid quantum neural network that integrates a pre-trained ResNet18 classical backbone with a quantum-enhanced classifier, implemented as a variational single-qubit circuit using the PennyLane framework. The model was trained on an augmented dataset and tested on original, non-augmented images to ensure a robust evaluation of its generalization capabilities. Experimental results demonstrate the superiority of the hybrid approach, which achieved an overall accuracy of 0.89, surpassing the 0.85 of the classical ResNet18 baseline, with notable performances on visually similar diseases, reaching an F1-score of 0.88 for rice stripes, 0.88 for rice blast, and 0.90 for rice tungro. These results lead to the conclusion that integrating even minimal quantum components can enhance classical deep learning models, enabling them to capture subtle feature representations. Future work will focus on exploring more complex quantum circuits with multi-qubit entanglement and deploying these models on Noisy Intermediate-Scale Quantum (NISQ) hardware to assess their real-world performance.

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