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.
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.
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.
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.
Ensuring crop health, optimizing yields, and facilitating sustainable agricultural practices depend on early detection and exact classification of rice diseases, particularly in South and Southeast Asian nations like India and Malaysia where rice is a dietary mainstay and economic backbone. Using a hybrid deep learning framework combining Convolutional Neural Networks (CNNs) with Quantum Neural Networks (QNNs) on AI edge devices, this article presents a unique real-time rice illness diagnosis and field suggestion system. While a parameterized quantum circuit incorporating Hadamard, CNOT, and Ry gates improves feature representation and classification resilience, a transfer learning technique using a pre-trained ResNet152 architecture allows effective feature extraction from rice leaf pictures. Included to reduce decoherence and noise influences are quantum error correcting methods as Shor’s Code and surface code approaches. Ensuring exact illness identification, the trained hybrid model has an F1 score of 96% and a total classification accuracy of 96.8%. Integrated recommendation logic links identified illnesses to customized agronomic prescriptions, including field treatments, nutrition management, and focused pesticide use. Deployment on AI edge devices allows completely offline, real-time inference, hence giving farmers instant actionable information in the field. Aiming to provide farmers with smart, scalable, and sustainable disease control tools, this paper underlines the transforming power of merging hybrid quantum-classical learning, transfer learning, and mobile edge artificial intelligence for precision agriculture.
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.
Corn leaf diseases are one of the biggest challenges undermining crop productivity and ensuring global food security, so early and accurate identification is crucial for proper management of the crop. Although Dense CNN and GoogleNet achieved high accuracies of 98.06% and 98.7%, respectively, the models fail to capture more complex relationships and patterns related to various leaf diseases because they use classical deep learning architectures. But with large datasets, the potential comes only in optimizing feature extraction and classification processes. With this, a need arises to introduce Quantum Convolutional Neural Networks (QCNN), where the principle of quantum computing can be utilized to enhance the representation of features for better detection accuracy. The demonstration of our experiments showed that the QCNN achieves a detection accuracy of 99%, superior to both Dense CNN and GoogleNet. Agriculture.
Potato leaf diseases pose a serious challenge to agricultural sustainability and food security in regions such as West Bengal, India, where potato cultivation plays a vital socio-economic role. Although classical deep learning models demonstrate strong performance, they often suffer from overfitting, limited generalization, and heavy reliance on large labeled datasets, while manual inspection remains time-consuming and error-prone. This work proposes a hybrid Quantum Convolutional Neural Network (QCNN) integrated with 4-qubit parameterized quantum circuits (PQCs) for accurate classification of five major potato leaf disease categories, including nutrient deficiency, fungal, and viral infections. The model incorporates quantum activation layers, quantum softmax, and a focused loss function to enhance non-linear feature representation and multi-class separability. Grad-CAM is employed to improve the interpretability of predictions. Experiments conducted on a diverse, field-collected dataset from Bardhaman, Bankura, and Purulia districts achieve a classification accuracy of 99.94%, demonstrating superior generalization, robustness, and explainability compared to classical approaches. This study highlights the practical potential of quantum-enhanced AI for precision agriculture.