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The European Physical Journal D

Quantum classifier for recognition and identification of leaf profile features

Amit Kumar, Nguyen Ngoc Mai, Ashmit Kumar, Nividita Varun Chand, Mansour H. Assaf

Published Jun 1, 2022• 14 citations
Publisher
DOI
10.1140/epjd/s10053-022-00429-z

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Early and accurate identification of mango leaf diseases is vital for safeguarding crop health, particularly in tropical agricultural regions like South India.This study presents M-HQNet, a novel Hybrid Quantum-Classical Convolutional Neural Network framework designed for the multiclass classification of mango leaf diseases.The pipeline begins with image preprocessing and data augmentation to improve model generalization.A pretrained MobileNetV2 is employed to perform deep feature extraction from augmented mango leaf images.To reduce dimensionality and align with quantum hardware constraints, Particle Swarm Optimization(PSO) is used, yielding a 4-dimensional feature vector optimized for quantum encoding.These selected features are embedded into a 4-qubit quantum circuit using a ZZFeatureMap and processed through a RealAmplitudes ansatz via angle encoding.The quantum circuit is implemented as a Hybrid-QCNN layer, followed by a Convolutional Neural Network (CNN) based classifier for final multiclass predictions.Experimental results demonstrate high effectiveness, achieving 96.2% accuracy.Comprehensive ablation studies and cross-validation confirm the critical contributions of PSO, quantum encoding, and the hybrid QCNN architecture.