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International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI)

Quantum Resnet18 for Classifying and Predicting the Maize Leaf Diseases

T. Kujani, Bhuvanya. R, T. Sathya, P. Arivubrakan, V. Usha, R. Thangaselvi

Published Apr 17, 2024• 7 citations
Publisher

Abstract

The agriculture industry contributes one-third of the worldwide Gross Domestic Product (GDP). Moreover, a substantial number of developing countries rely on their agricultural output as it offers job prospects for a considerable portion of the impoverished population. This necessitates the use of techniques to guarantee the precise and effective identification of plant diseases to minimize any detrimental consequences on the crops. Recently, the application of quantum-enhanced techniques to machine learning tasks including recommendation systems, classification, and regression has increased significantly. For this reason, this work suggests using the hybrid quantum-based Resnet18 transfer learning model to categorize and diagnose diseases that impact the leaves of maize plants. The dataset employed consists of four categories including Leaf Blight, Grey Leaf spots, Common Rust, and Healthy leaves. When compared to Resnet18 without quantum, this method outperforms it in terms of recall, accuracy, precision, and f1-score. The validation accuracy of 87% is achieved using the model. In the future, the proposed effort can serve as a practical tool to aid farmers in recognizing the diseases and safeguarding their maize crops.

DOI
10.1109/raeeucci61380.2024.10547895

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•Mar 23, 2026•0 citations
The agriculture industry contributes one-third of the worldwide Gross Domestic Product (GDP). Moreover, a substantial number of developing countries rely on their agricultural output as it offers job prospects for a considerable portion of the impoverished population. This necessitates the use of techniques to guarantee the precise and effective identification of plant diseases to minimize any detrimental consequences on the crops. Recently, the application of quantum-enhanced techniques to machine learning tasks including recommendation systems, classification, and regression has increased significantly. For this reason, this work suggests using the hybrid quantum-based Resnet18 transfer learning model to categorize and diagnose diseases that impact the leaves of maize plants. The dataset employed consists of four categories including Leaf Blight, Grey Leaf spots, Common Rust, and Healthy leaves. When compared to Resnet18 without quantum, this method outperforms it in terms of recall, accuracy, precision, and f1-score. The validation accuracy of 87% is achieved using the model. In the future, the proposed effort can serve as a practical tool to aid farmers in recognizing the diseases and safeguarding their maize crops.
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.
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.
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
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%{9 6 \%}96% and a total classification accuracy of 96.8%96.8 \%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.
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.
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.