Pest classification holds immense significance in agriculture, serving as a cornerstone in safeguarding crop health and global food security. This paper introduces a hybrid classical- quantum machine learning model with the objective of revolutionizing pest classification. The proposed methodology embraces the Classical-Quantum (CQ) paradigm, amalgamating quantum circuit design, the Quantum-Enhanced Classical Network (QCN), and seamless integration with the MobileNetV2architecture. Remarkably, this hybrid model outperforms MobileNetV2 in terms of accuracy, boasting an exceptional training accuracy of 98% and an impressive testing accuracy of 96%. These achievements are realized on a dataset encompassing 4263 images of tomato-specific pests categorized into 8 distinct classes. The CQ model attains this remarkable performance by harnessing the power of quantum computing to extract more distinctive features from pest images. The QCN component of the model facilitates efficient and scalable training of quantum- enhanced classical classifiers, contributing to its superior performance. The integration of the MobileNetV2 architecture provides the model with a lightweight and efficient architecture that is suitable for deployment on mobile devices.
Pest classification holds immense significance in agriculture, serving as a cornerstone in safeguarding crop health and global food security. This paper introduces a hybrid classical- quantum machine learning model with the objective of revolutionizing pest classification. The proposed methodology embraces the Classical-Quantum (CQ) paradigm, amalgamating quantum circuit design, the Quantum-Enhanced Classical Network (QCN), and seamless integration with the MobileNetV2architecture. Remarkably, this hybrid model outperforms MobileNetV2 in terms of accuracy, boasting an exceptional training accuracy of 98% and an impressive testing accuracy of 96%. These achievements are realized on a dataset encompassing 4263 images of tomato-specific pests categorized into 8 distinct classes. The CQ model attains this remarkable performance by harnessing the power of quantum computing to extract more distinctive features from pest images. The QCN component of the model facilitates efficient and scalable training of quantum- enhanced classical classifiers, contributing to its superior performance. The integration of the MobileNetV2 architecture provides the model with a lightweight and efficient architecture that is suitable for deployment on mobile devices.
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 land classification from satellite imagery is essential for applications in agriculture, environmental management, and urban planning. Traditional lightweight models such as MobileNet provide computational efficiency but often struggle to capture the fine-grained spatial features necessary for reliable classification. This study proposes a quantum-augmented MobileNet framework that leverages quantum circuits to enhance feature representation. The methodology involves preprocessing satellite images and encoding them into quantum states using parameterized quantum circuits that apply superposition and entanglement to capture complex spatial relationships. These enriched features are then processed through a streamlined MobileNetV2 architecture optimized for multi-class classification across ten land cover categories. The model is trained and evaluated using the EuroSAT dataset consisting of 27,000 images. Experimental outcomes show that the hybrid quantum-classical approach improves model robustness and classification performance without increasing computational overhead. This integration offers a scalable and efficient solution for real-time remote sensing applications, demonstrating the promise of quantum computing in advancing geospatial intelligence.
This paper presents a systematic empirical benchmarking of quantum machine learning (QML) applications within the under-explored domain of agricultural image classification. The authors implement and evaluate four hybrid quantum-classical architectures—including variational quantum classifiers (VQC), quanvolutional neural networks (QNN), and a ResNet-50 combined with a Quantum Support Vector Machine (QSVM)—against a purely classical EfficientNet baseline. The architectures are tested on three agricultural disease datasets: potato tuber disease, jujube insect pests, and paddy leaf rust. The experimental findings reveal that the ResNet-50+QSVM hybrid model achieves the optimal balance of classification accuracy and training stability, reaching a 93.4% mean accuracy. Crucially, the hybrid QSVM demonstrates extreme sample efficiency by requiring 41% fewer labeled training samples than the classical EfficientNet baseline to cross a 90% accuracy threshold, showing the capability of quantum feature maps in low-data regimes.
Agriculture faces increasing risks in ensuring global food security, with challenges such as climate change, biodiversity loss, and plant disease and pest outbreaks posing significant threats to crop yields. These factors are compounded by growing global populations, resource limitations, and market fluctuations. Traditional methods for crop pest and disease monitoring, often reliant on manual surveys and basic machine learning models, are inadequate in addressing these challenges due to limited scalability and accuracy. This research explores the potential application of quantum machine learning (QML) for crop pest and disease monitoring, leveraging the advanced computational capabilities of quantum computers to improve detection accuracy, efficiency, and scalability. The study reviews existing monitoring systems, recent advancements in quantum computing technologies, and the integration of QML algorithms, which offer promising solutions for handling large, complex agricultural datasets. While the practical application of QML in agriculture is still in its early stages, its theoretical advantages indicate significant potential for future smart agricultural systems.
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.
Classification of crops and weeds is an important aspect in precision agriculture, as it aids in providing a means for efficient weed control and reduced herbicide applications to achieve high crop productivity. In this paper, a quantum-classical hybrid deep learning approach is presented for accurate identification of crops and weeds from sorghum images using the dataset Sorghum Crop Weed Dataset_Classification. In this hybrid framework, a convolutional neural network (CNN) for feature extraction in the spatial domain, along with a variational quantum circuit (VQC) to provide better feature representation and classification abilities in higher-dimensional quantum space, is used. This allows for effective differentiation of crop and weed instances that appear alike in different agricultural conditions. Data preprocessing methods like resizing, normalization, and data augmentation (rotation, flipping, and brightness adjustment) are performed to ensure uniformity in data and increase the generalizability of the trained model. According to experimental evaluations conducted using traditional metrics, the designed model attains an accuracy of 97.3%, precision of 96.9%, recall of 97.2%, F1-score of 97.1%, and a training loss of 0.09. The results show a significant increase from baseline approaches such as CNN (92.5% accuracy), ResNet (94.0% accuracy), and transfer learning (95.0% accuracy). In addition, the designed model performs well when tested under challenging situations encountered in the real-world agricultural environment, such as varying illuminations, partial occlusion, and background noises. In summary, the above results prove the success of combining classical deep learning and quantum feature learning to enhance classification performance.