Quantum machine learning (QML) is receiving increasing attention as a potential tool for addressing the computational challenges inherent in agricultural remote sensing, where multimodal Earth observational satellite, soil, and climate datasets must be processed at scale. We review recent developments in quantum–classical hybrid modeling for core remote-sensing tasks, including crop type classification, yield estimation, cloud detection, and time series forecasting. Researchers typically combine classical feature extractors with shallow variational circuits or quantum kernels to accommodate the limited qubit counts and noise levels of contemporary quantum devices. We summarize performance trends reported in the literature, emphasizing the role of classical preprocessing, compact quantum circuits, and rigorous benchmarking against strong classical baselines. We consolidate the reviewed studies in a structured comparison covering task, dataset, qubit count, encoding, execution platform, and reported performance relative to classical baselines. The current hardware and data limitations that restrict current QML applications associated with state preparation, circuit depth, and encoding of agricultural datasets are discussed. Near-term prospects for QML in agriculture can involve small-scale hybrid models for multi-class crop mapping and short horizon sequence prediction, while longer-term opportunities may encompass quantum accelerated image segmentation, data fusion, and large scale optimization. Current evidence indicates that QML performance at best matches strong classical baselines, with the most plausible near-term utility arising in narrow settings such as severely label-limited learning and QUBO-structured optimization. Future gains will depend jointly on hardware maturation, data encoding strategies, algorithm design, and integration with classical AI systems.
Quantum machine learning (QML) is receiving increasing attention as a potential tool for addressing the computational challenges inherent in agricultural remote sensing, where multimodal Earth observational satellite, soil, and climate datasets must be processed at scale. We review recent developments in quantum–classical hybrid modeling for core remote-sensing tasks, including crop type classification, yield estimation, cloud detection, and time series forecasting. Researchers typically combine classical feature extractors with shallow variational circuits or quantum kernels to accommodate the limited qubit counts and noise levels of contemporary quantum devices. We summarize performance trends reported in the literature, emphasizing the role of classical preprocessing, compact quantum circuits, and rigorous benchmarking against strong classical baselines. We consolidate the reviewed studies in a structured comparison covering task, dataset, qubit count, encoding, execution platform, and reported performance relative to classical baselines. The current hardware and data limitations that restrict current QML applications associated with state preparation, circuit depth, and encoding of agricultural datasets are discussed. Near-term prospects for QML in agriculture can involve small-scale hybrid models for multi-class crop mapping and short horizon sequence prediction, while longer-term opportunities may encompass quantum accelerated image segmentation, data fusion, and large scale optimization. Current evidence indicates that QML performance at best matches strong classical baselines, with the most plausible near-term utility arising in narrow settings such as severely label-limited learning and QUBO-structured optimization. Future gains will depend jointly on hardware maturation, data encoding strategies, algorithm design, and integration with classical AI systems.
Accurate soil nutrient estimation underpins fertility assessment, precision agriculture, and sustainable land management, yet conventional laboratory analysis is slow, costly, and impractical at scale. Hyperspectral imaging (HSI) captures detailed spectral signatures linked to soil properties, but its high dimensionality and limited ground truth samples strain classical machine learning. This review examines quantum machine learning (QML) as an emerging rather than established direction for high-dimensional, low-sample hyperspectral soil analysis. We outline where classical machine learning and deep learning fall short and then assess how quantum kernel methods, variational quantum circuits, and hybrid quantum–classical architectures might improve feature representation and nonlinear modeling. Of the 150 studies reviewed, 26 report quantum machine learning results of any kind, five use soil data, and only one evaluates soil spectra directly for nutrient or property estimation. The remainder contribute transferable evidence from adjacent soil and remote sensing tasks, together with soil applications that remain proposals. Accordingly, the review weighs both the promise and the practical constraints of QML on current noisy intermediate-scale quantum hardware, including the encoding cost, measurement overhead, circuit depth, and trainability limits. Future research priorities are identified: quantum-ready hyperspectral soil datasets, reproducible benchmarking against well-tuned classical baselines, scalable hybrid pipelines, hardware-aware reporting, and field validation. Pairing QML with hyperspectral soil sensing may eventually support soil fertility assessment, variable-rate fertilization, and sustainable precision agriculture, but any such benefit must be demonstrated experimentally rather than inferred and will depend on continued progress in quantum hardware.
This paper introduces a hybrid machine learning and quantum, inspired framework for the prediction of crop yield and profit in precision agriculture. The presented system combines classical machine learning models with quantum, inspired learning such that the model's predictability and generalization can be increased. Random Forest regressors are employed to estimate crop yield and economic profit, thus providing a stable and reliable baseline for the performance. Quantum Kernel Ridge Regression and a Variational Quantum Circuit have been added to this framework as corrective components to enhance the model's generalization. The proposed technique involves methodical data preprocessing: categorical attributes are encoded (soil type, season, crop name, and disease status), and numerical features are scaled (rainfall, nutrient level, fertilizer usage, and cultivated area). Instead of using image, based disease detection methods, disease condition is considered as a structured input feature and is merged with the yield and profit estimation process. The quantum-inspired module, which is implemented with Qiskit, employs Principal Component Analysis for reducing dimensions and utilizes a ZZFeatureMap to encode quantum features to construct quantum kernels. By providing an effective framework for data-driven agricultural decision support, the current system is deployed via an interactive Streamlit application for dataset uploading, model training and prediction, and visual analysis through correlation heatmaps and yield-profit trend plots. Experimental results have shown that a hybrid integration of classical machine learning and quantum- inspired methodology could enhance predictability, interpretability, and practical usability, thus offering this framework suitable for data-driven agricultural decision support.
Agriculture is increasingly threatened by diverse global security challenges, including climate change, biodiversity loss, and health security risks, underscoring the need for sustainable practices to ensure food security for future generations. Advanced technologies such as quantum machine learning (QML) offer transformative potential in addressing these challenges by enhancing crop disease monitoring systems. This paper explores the implementation of QML in agriculture, with a particular focus on its application to crop disease detection and prevention. Leveraging image processing, sensory data analysis, and spectral imaging, QML algorithms demonstrate superior accuracy and efficiency in early disease recognition, surpassing traditional methods. A case study of Hungary's crop disease monitoring system illustrates the practical benefits of integrating QML with technologies such as remote sensing and IoT. By enabling real-time monitoring and predictive analytics, QML not only aids in mitigating crop losses but also contributes to sustainable agricultural practices and global food security. Despite current limitations in quantum hardware, advancements in QML present promising opportunities for revolutionizing agricultural systems and ensuring resilience against evolving threats.
Small objects have become increasingly common in civil, industrial, and security sensitive environments, driving a strong demand for reliable vision-based detection and segmentation. While deep learning (DL) models have achieved impressive performance, their practicality is limited by the small sizes of objects, cluttered backgrounds, motion blur, domain shift, and the growing cost of training and inference. Quantum machine learning (QML) has recently emerged as a potential research direction for exploring alternative feature representations, optimization strategies, and learning paradigms under resource constrained conditions. However, its practical efficiency benefits for object detection and segmentation remain largely preliminary and context dependent. The literature on quantum methods for detection and segmentation is scattered across the QML, aerial vision, and remote sensing domains. We provide a structured review of quantum, hybrid quantum–classical (HQC), and quantum approaches for detection and segmentation. We classify existing methods based on four aspects: how features are encoded and represented in quantum systems, the use of quantum kernels, variational quantum circuits (VQC) for classification and detection, and quantum architectures combined with modern detection and segmentation models. We summarize the commonly used datasets for quantum techniques, evaluation protocols, and computational constraints, comparing the reported performance trends, scalability levels, and practical limitations of quantum-based methods. Finally, we identify the open challenges, including data-efficient learning, robustness to domain shift, and end-to-end quantum segmentation, along with the research directions relevant to bridging the gap between QML and real-world object perception systems.
Artificial intelligence (AI) has achieved remarkable progress in single-modal tasks such as text analysis and image recognition. Several real-world problems often demand multimodal intelligence that can integrate and reason across diverse data sources. Traditional machine learning methods face scalability and efficiency limitations for handling heterogeneous multimodal datasets. Recent work in quantum computing introduces new opportunities for AI using superposition and entanglement. Quantum Machine Learning (QML) has shown early promise in classification, optimization, and learning, but applications to multimodal intelligence remain at an early stage. Existing studies are fragmented, and there is a lack of systematic understanding of how QML can address the challenges of multimodal learning. Therefore, this paper provides a structured survey of QML approaches for multimodal tasks. We review data encoding strategies, quantum models such as quantum support vector machines and variational quantum circuits, and hybrid quantum-classical frameworks. We analyze their strengths, limitations, current applications, and future directions. Our findings show that QML faces obstacles in scalability, noise, and benchmarking for efficient cross-modal reasoning and representation. Quantum generative models and quantum-inspired multimodal architectures could significantly expand the capabilities of AI in the future. It highlights both the opportunities and open challenges in applying QML to multimodal intelligence. Overall, the paper provides researchers with a roadmap for advancing this field and sets the stage for future applications in precision medicine, autonomous systems, cybersecurity, and climate modeling.
This research presents a hybrid variational quantum machine learning framework for real-world soil condition prediction using hyperspectral and environmental data. The study integrates classical preprocessing techniques with a Variational Quantum Classifier (VQC) implemented using Qiskit to classify soil moisture conditions into dry and wet states. A real soil moisture dataset containing environmental and spectral features was utilized to evaluate the model’s performance. Data normalization and feature encoding were applied before embedding the features into a parameterized quantum circuit composed of a ZZFeatureMap and TwoLocal ansatz. Model optimization was performed using the COBYLA optimizer within a simulator-based quantum backend. The system demonstrates the feasibility of applying near-term quantum machine learning techniques to agricultural analytics. Performance evaluation includes confusion matrix analysis, accuracy, precision, recall, and F1-score metrics. The study further discusses scalability considerations, simulator-based execution constraints, and the implications of deploying the approach on real quantum hardware. This work contributes to the intersection of quantum computing, precision agriculture, and hyperspectral data analytics, highlighting the potential of hybrid quantum-classical workflows for next-generation intelligent farming systems.