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International Conference on Soft Computing for Security Applications (ICSCSA)

A Hybrid Quantum–Classical Neural Network for Chilli Leaf Disease Classification: A Variational Quantum Feature-Fusion Approach

J. Senthilkumar, Arjun S, Loshitha K, Dhanasri R, Mohammed Faizal

Published Aug 1, 2026• 0 citations
Publisher
DOI
10.1109/icscsa69367.2026.11707988

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Reduced crop losses and guaranteeing agricultural productivity depend on the early and accurate detection of plant diseases. This work offers a Hybrid Quantum-Classical HQC-CNN, or convolutional in nature neural network that uses image analysis to determine various diseases of apple leaves. The model is executed using the PennyLane a system which blends quantum computing techniques with conventional deep computing methods. In the classical segment, layers for collaboration and convolution were used to extract significant spatial features from normalised and resized leaf images.These features are mapped into quantum states by Angle Embedding, and intricate patterns are revealed by StronglyEntanglingLayers processing them inside an electromagnetic circuit. The quantum information layer's output is sent to a softmax classifier, which divides the input into different disease groups using Pauli-Z expectation value measurements. The framework has been examined and developed with a publicly available dataset of apple leaf images, and it demonstrated strong performance on several evaluation parameters, such as the F1-s remembering, reliability, and quality. These findings show how well the model can identify a range of leaf diseases and point to how it could help precision farming and the broader field of quantum improved neural networks.

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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.
Reduced crop losses and guaranteeing agricultural productivity depend on the early and accurate detection of plant diseases. This work offers a Hybrid Quantum-Classical HQC-CNN, or convolutional in nature neural network that uses image analysis to determine various diseases of apple leaves. The model is executed using the PennyLane a system which blends quantum computing techniques with conventional deep computing methods. In the classical segment, layers for collaboration and convolution were used to extract significant spatial features from normalised and resized leaf images.These features are mapped into quantum states by Angle Embedding, and intricate patterns are revealed by StronglyEntanglingLayers processing them inside an electromagnetic circuit. The quantum information layer's output is sent to a softmax classifier, which divides the input into different disease groups using Pauli-Z expectation value measurements. The framework has been examined and developed with a publicly available dataset of apple leaf images, and it demonstrated strong performance on several evaluation parameters, such as the F1-s remembering, reliability, and quality. These findings show how well the model can identify a range of leaf diseases and point to how it could help precision farming and the broader field of quantum improved neural networks.