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Indian Journal of Computer Science and Technology

Hybrid AI–Quantum Architecture for Sustainable Plant Disease Detection and Pesticide Recommendation

Jahnavi Sarasa, challa Ratnakumari

Published Nov 1, 2026• 0 citations
Publisher

Abstract

Plant diseases, pest attacks, and nutrient imbalances significantly affect agricultural productivity and can reduce both yield and quality. Timely and accurate identification of these issues is essential for effective crop management and sustainable farming. Conventional manual inspection methods are often inefficient and inconsistent, while existing deep learning approaches may struggle to differentiate between diseases with similar visual characteristics, leading to inaccurate predictions and suboptimal treatment choices. To overcome these limitations, this work introduces a hybrid classical–quantum framework aimed at improving disease detection and decision-making. The proposed system uses EfficientNet-B0 to learn rich visual representations from plant images, then processes them with a variational quantum circuit to capture complex, nonlinear feature interactions. By integrating classical and quantum techniques, the model enhances classification performance beyond traditional methods. Beyond accurate disease identification, the system provides environmentally conscious pesticide recommendations, supporting responsible chemical use. Overall, this approach serves as an effective intelligent decision-support system, contributing to more precise and sustainable agricultural practices.

DOI
10.59256/indjcst.20260503073

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Plant diseases, pest attacks, and nutrient imbalances significantly affect agricultural productivity and can reduce both yield and quality. Timely and accurate identification of these issues is essential for effective crop management and sustainable farming. Conventional manual inspection methods are often inefficient and inconsistent, while existing deep learning approaches may struggle to differentiate between diseases with similar visual characteristics, leading to inaccurate predictions and suboptimal treatment choices. To overcome these limitations, this work introduces a hybrid classical–quantum framework aimed at improving disease detection and decision-making. The proposed system uses EfficientNet-B0 to learn rich visual representations from plant images, then processes them with a variational quantum circuit to capture complex, nonlinear feature interactions. By integrating classical and quantum techniques, the model enhances classification performance beyond traditional methods. Beyond accurate disease identification, the system provides environmentally conscious pesticide recommendations, supporting responsible chemical use. Overall, this approach serves as an effective intelligent decision-support system, contributing to more precise and sustainable agricultural practices.
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.
This study addresses the critical need for accurate and interpretable plant disease detection in sustainable agriculture. Existing deep learning models often suffer from limited generalization across crops, insufficient modeling of complex feature interactions, and a lack of explainability, restricting their real-world applicability. To overcome these challenges, this work proposes TIGLeafNet-Q, a hybrid quantum–classical framework for multi-crop leaf disease detection. The proposed approach integrates an InceptionV3 transfer learning backbone for deep feature extraction, followed by feature compression and angle encoding into quantum states. A variational quantum circuit (VQC) is employed to capture higher-order correlations among disease features, and Grad-CAM is used to provide visual interpretability. The model is evaluated on large-scale tomato (22,980 images) and cotton (2,204 images) datasets. Experimental results demonstrate classification accuracies of 99.28% (tomato) and 97.23% (cotton), outperforming existing methods. The proposed framework contributes a scalable, explainable, and multi-crop capable solution, highlighting the potential of hybrid quantum–classical learning in agricultural intelligence systems.
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.
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.