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International Conference on Computing, Communication and Automation

Hybrid Quantum-Classical Convolutional Neural Network (HQC-CNN) for Apple Leaf Disease Classification

Saurabh Srivastava, Rajeev Kumar, Abu Bakar Abdul Hamid

Published Nov 28, 2025• 0 citations
Publisher

Abstract

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.

DOI
10.1109/iccca66364.2025.11325703

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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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Medical and agricultural sector needs to make timely and accurate diagnosis of the disease in order to sustain crops and keep patients alive. Although conventional deep learning models can be also applicable to provide good performance, they are also typified with high-computational and scaling along with high-dimensionality agricultural and medical images. In a bid to overcome these challenges, the current paper proposes a Hybrid Quantum -Classical Artificial Intelligence (HQCAI) system that should be used in dual-domain image classification, namely, plant leaf disease classification and oesophageal cancer classification. The proposed architecture implements a conventional Convolutional Neural Network (CNN) to obtain spatial features of high quality and quantum-enhanced classifiers to optimize the decision boundaries relying on quantum superposition and parallelism with Variational Quantum Circuits (VQCs) and Quantum Support Vector Machines (QSVMs). It presents a common knowledge representation layer in order to help in the effective storage of characteristics, cross-domain learning and intelligent inference between heterogeneous data sets. Experiments are carried out with the help of the data collection. The proposed hybrid model gives a better classification rate of 98.42, with precision of 98.11, recall of 97.94 and F1-score of 98.02 compared to CNN and CNN-SVM models in the state of the art. Convergence and performance of the system is also better when noises are present. The results support the practical role of quantum-enhanced visual classification and describe the prospects of hybrid quantum classical intelligence of the agricultural diagnostics and medical diagnostics systems in the future.
This study investigates the effectiveness of a hybrid quantum-classical framework for plant disease detection, with a focus on improving classification accuracy compared to conventional approaches. Image-based datasets of crop diseases were processed and encoded into variational quantum circuits (VQCs), which were then integrated with classical machine learning classifiers. The proposed model achieved an overall classification accuracy of 95.2%, outperforming a baseline convolutional neural network that recorded 91.4% accuracy on the same dataset. Performance evaluation was carried out using multiple metrics including precision, recall, and F1-score, with the hybrid approach showing consistent gains across all categories. Furthermore, the model demonstrated reduced overfitting and improved robustness when tested under noisy and high-dimensional input conditions. These results highlight the potential of quantum-assisted machine learning to significantly enhance crop disease detection, offering a scalable and accurate pathway for sustainable agriculture and precision farming.
Plant leaf diseases are a critical threat to global food security, with early detection complicated by high inter-class similarity, environmental variations, and the demand for scalable multiclass classification across diverse crops. To address these challenges, we propose P-HQNN, a lightweight yet robust 4-qubit Hybrid Quantum–Classical Convolutional Neural Network designed for near-term quantum devices. The model uses EfficientNet-B0 to extract deep feature, and Particle Swarm Optimization (PSO) with Random Forest ranking is used to reduce the 1280-dimensional embeddings into four highly discriminative features that can be represented using quantum encoding. These features are next coded as a 4-qubit quantum circuit of ZZFeatureMap and RealAmplitudes ansatz and a hybrid quantum layer is formed whose outputs are concatenated with a CNN classifier to arrive at final predictions. This optimization strategy of features representation based on quantum awareness balances high-dimensional representation and qubit constraints, and is proved to be more stable in 4-qubit encoding and better in comparison to higher-qubit encoding under noisy conditions. Experimental evaluation on the “New Plant Diseases Dataset” achieved 98.2% accuracy, 98.5% precision, 97.7% recall, 98% F1-score, and a macro ROC-AUC of 0.985, confirming that low-qubit hybrid models can deliver state-of-the-art plant disease classification while ensuring practicality and scalability for real-world agricultural applications on near-term quantum computing platforms.
Indonesia is an agricultural country that was once listed as the third largest country in terms of rice production. Several factors, such as pests and diseases, are factors in Indonesia's decline in rice productivity. This research explores the integration of quantum computing into classical convolutional neural networks (CNN) for the classification of rice leaf diseases. The study utilizes a hybrid quantum neural network (HQNN) model by combining classical CNN with quantum circuits to evaluate the effect of quantum computing in enhancing agricultural disease detection. The dataset comprises 5932 images of rice leaves affected by four distinct diseases, with each image standardized to 300×300 pixels. The HQNN models were benchmarked against traditional CNN, significantly improving classification accuracy. HQNN reached the best training accuracy of 99.93% and validation accuracy of 99.83%. These findings show that quantum computing has great potential in terms of performance, especially when quantum hardware becomes more accessible. The research concludes by suggesting the need for further exploration with more extensive and diverse datasets to realize the benefits of HQNN in diverse agricultural applications fully.