A hybrid quantum–classical convolutional neural network with EfficientNet-B0 and PSO-based feature optimization for multiclass plant leaf disease classification
G. Suryanarayana, LNC Prakash K, Saikiran Gogineni, Dr. N. Swapna, A.Vijaya Krishna
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.
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.
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.
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.
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.
Detecting plant diseases is a crucial component of precision agriculture, necessary for maintaining economic viability, sustainable farming practices, food security, and preventing economic losses. Deep learning models such as Capsule Attention Networks, DenseNets, and Convolutional Neural Networks have demonstrated state-of-the-art performance in leaf-image-based disease classification. However, large-scale practical implementation in resource-constrained environments remains challenging due to overfitting, environmental insensitivity, extended training times, and high computational cost, along with the absence of a sustainable framework for real-world deployment. Emerging quantum neural networks (QNNs), integrated with quantum-influenced optimization algorithms, offer promising avenues to enhance practical applicability. Their capabilities for high-dimensional feature mapping, entanglement, and superposition improve generalization and convergence in hybrid models that combine quantum layers with classical architectures. Incorporating quantum modules into DenseNets, attention mechanisms, capsule networks, and other hybrid systems may further enhance accuracy, enable robust disease segmentation, and reduce training time. Nonetheless, challenges remain regarding real-time deployment on edge devices, efficient data encoding, limited explainability of quantum-driven features, and hardware constraints. This survey reviews classical, hybrid, and quantum-enhanced approaches, provides comparative insights, and identifies research gaps toward developing scalable, interpretable, and dependable frameworks for plant disease detection and diagnosis.
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.
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.