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 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.
The need for accuracy and scalability is imperative when it comes to monitoring soil health and detecting plant diseases using precision agriculture. This paper suggests a Quantum AI-based hybrid analytics approach that combines classical machine learning algorithms, Variational Quantum Circuits, and statistics modeling in order to provide improved agricultural decision making capabilities. For this purpose, we used the multi-modal datasets that include Soil Health Data set (Kaggle dataset, n=12,500) that contains soil pH, moisture, Nitrogen (N), Phosphorus (P), Potassium (K) information and PlantVillage dataset (54,306 leaf images from 38 classes). This paper proposes a hybrid framework of QSVM and QCNN to predict soil health status and detect crop diseases in a dataset. This research work has been experimentally evaluated with Qiskit simulator (8 qubits) and classical ML algorithms like Random Forest and CNN. Based on experimental results, it is clear that the proposed hybrid framework yields soil health prediction accuracy of 94.7%, which is better than classical ML approaches (88.3%). Further, the accuracy achieved by the QCNN framework for predicting crop disease is 97.2%, with precision, recall, and F1-score being 0.96, 0.97, and 0.965, respectively. It is observed that the computation time is reduced by around 21.4% in feature selection tasks. From the above discussion, it can be concluded that this paper explores the capability of Quantum Artificial Intelligence (AI) to provide timely assistance for precision farming applications.
This paper presents a novel approach to crop disease classification through the integration of quantum computing principles with deep learning techniques. We introduce a hybrid quantum neural network architecture that leverages quantum mechanical phenomena, including superposition and entanglement, to enhance crop disease detection capabilities. Using a comprehensive dataset of 23,265 images spanning 19 disease classes across five major crop species, our model achieved a test accuracy of 0.7958 with a weighted average F1-score of 0.85. The architecture combines classical convolutional layers for initial feature extraction with quantum processing units for advanced pattern recognition, demonstrating significant potential in the diagnosis of agricultural diseases. Our findings establish the viability of quantum-enhanced neural networks in agricultural applications, while highlighting pathways for future development in quantum machine learning systems.
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.
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.
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.