<p>This paper introduces an efficient and impactful method that uses the Atangana-Baleanu fractional integral image enhancement technique combined with the quantum convolutional neural network (QCNN) for bell pepper leaf disease classification. The proposed approach aims to improve the classification accuracy of plant leaves by addressing the challenges posed by low-contrast images. The proposed image enhancement (IE) approach is employed to improve the contrast, sharpness, and overall quality of the images. The enhanced images are then fed into the proposed QCNN for disease classification. The classification of pepper leaf diseases achieves significant efficacy by leveraging the power of the proposed QCNN when working with enhanced images. The proposed algorithm outperforms traditional convolutional neural networks (CNNs) and other IE techniques in terms of accuracy of 97.64%, specificity of 98.36%, precision of 98%, recall of 96.98%, and F1 score of 98.18%.</p>