Classifying rice plant disease from leaf images presents a significant challenge due to the high visual similarity between different disease types, which can limit the effectiveness of purely classical deep learning models. This research addresses this problem by developing and evaluating a hybrid quantum neural network that integrates a pre-trained ResNet18 classical backbone with a quantum-enhanced classifier, implemented as a variational single-qubit circuit using the PennyLane framework. The model was trained on an augmented dataset and tested on original, non-augmented images to ensure a robust evaluation of its generalization capabilities. Experimental results demonstrate the superiority of the hybrid approach, which achieved an overall accuracy of 0.89, surpassing the 0.85 of the classical ResNet18 baseline, with notable performances on visually similar diseases, reaching an F1-score of 0.88 for rice stripes, 0.88 for rice blast, and 0.90 for rice tungro. These results lead to the conclusion that integrating even minimal quantum components can enhance classical deep learning models, enabling them to capture subtle feature representations. Future work will focus on exploring more complex quantum circuits with multi-qubit entanglement and deploying these models on Noisy Intermediate-Scale Quantum (NISQ) hardware to assess their real-world performance.
Classifying rice plant disease from leaf images presents a significant challenge due to the high visual similarity between different disease types, which can limit the effectiveness of purely classical deep learning models. This research addresses this problem by developing and evaluating a hybrid quantum neural network that integrates a pre-trained ResNet18 classical backbone with a quantum-enhanced classifier, implemented as a variational single-qubit circuit using the PennyLane framework. The model was trained on an augmented dataset and tested on original, non-augmented images to ensure a robust evaluation of its generalization capabilities. Experimental results demonstrate the superiority of the hybrid approach, which achieved an overall accuracy of 0.89, surpassing the 0.85 of the classical ResNet18 baseline, with notable performances on visually similar diseases, reaching an F1-score of 0.88 for rice stripes, 0.88 for rice blast, and 0.90 for rice tungro. These results lead to the conclusion that integrating even minimal quantum components can enhance classical deep learning models, enabling them to capture subtle feature representations. Future work will focus on exploring more complex quantum circuits with multi-qubit entanglement and deploying these models on Noisy Intermediate-Scale Quantum (NISQ) hardware to assess their real-world performance.
Early and accurate diagnosis of plant diseases is super important for boosting crop productivity and cutting down on economic losses. Deep learning models have really nailed image-based disease detection lately, but they face some hurdles like complex feature distributions and class imbalance in ag data sets. This study takes a neat approach to solve those issues – a hybrid quantum-classical deep learning framework. It combines a pretrained DenseNet121 with a Variational Quantum Circuit (VQC). The DenseNet121 grabs key visual features from leaf images, while the quantum part does fancy nonlinear feature transformations in a high-dimensional quantum space. To make the model tougher and more adaptable, the researchers used Mixup augmentation, label smoothing, and balanced sampling when training it. Their tests on the Plant Pathology dataset scored an 89% accuracy overall, with precision, recall, and F1-score coming in at 88%, 87%, and 88%, respectively. So it seems hybrid quantum-classical learning could seriously beef up plant disease classification and maybe even pave the way for smarter ag diagnostic tools in the future
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.
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.
This study presents a novel hybrid quantum‐classical framework for rice disease diagnosis, leveraging variational quantum circuits (VQCs) to address the limitations of traditional and deep learning models in precision agriculture. The proposed Quantum Variational Rice Disease Network (QVRDN) integrates quantum feature encoding, variational quantum processing, and adaptive optimization to achieve superior classification accuracy, efficiency, and robustness. Using a curated dataset of 3000 annotated rice leaf images spanning major disease categories, the QVRDN framework applies dimensionality reduction and quantum angle encoding to transform the image features into quantum states, which are then processed by parameterized quantum circuits for disease classification. Experimental results demonstrate that QVRDN outperforms classical models, including SVM, random forest, CNN, and ResNet50‐achieving, the highest accuracy of 97.8%, faster inference times, and greater resilience to noise and limited data. The compact design of the framework enables edge deployment without GPU dependency, making it suitable for resource‐constrained agricultural environments. By demonstrating the feasibility and advantages of quantum machine learning in crop health monitoring, this study establishes a foundation for quantum‐enhanced, data‐efficient agricultural diagnostics and paves the way for future advances in intelligent, field‐ready quantum geoinformatics systems.
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.
Accurate classification of microscopic blood cells is still a critical task in medical image analysis, where subtle variations and limited data can challenge conventional deep learning models. As such, we investigate in this work the potential of Hybrid Quantum-Classical Neural Networks (HQNNs) to enhance feature representation and improve classification performance in this domain. We propose a modular architecture combining a pre-trained ResNet-50 backbone with a low-dimensional latent bottleneck and a variational quantum circuit, enabling a direct comparison between quantum-enhanced and purely classical transformation mechanisms. To isolate the contribution of the quantum component, we evaluate three architectures: a HQNN model, a Classical Matched Model with an additional nonlinear transformation layer of comparable capacity, and a baseline model without an intermediate transformation stage. Experiments conducted on two publicly available blood cell datasets, namely the Blood Cell Images dataset and the PBC dataset, demonstrate that HQNNs consistently achieve superior or more balanced performance across evaluation metrics. In the Blood Cell Images Dataset, the proposed approach improves macro F1-score by up to 3.7% compared to classical baselines, while improving the F1-score from 98.54% to 98.69% in the more challenging 8-class scenario with near-saturated performance. Additional evaluation on IBM quantum hardware shows that the model remains robust under noise, with only a modest performance degradation relative to simulated results. These results indicate that quantum feature transformations can enhance discriminative representations, particularly in challenging classification scenarios, and highlight the practical potential of HQNN models for medical imaging tasks.