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.
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.
Contemporary agriculture is having major difficulties in assuring nutrient delivery and caring for the environment. The paper proposes integrating quantum machine learning (QML) techniques with precision agriculture tools to bring improvements to nutrient management. Predicting the correct amount, timing and distribution of fertilizer works much better with our new quantum-based strategy than with established ones. Analyzing the interactions among soil, plants and the atmosphere with quantum variational circuits and quantum kernel methods allows us to predict how much fertilizer is needed with an accuracy of 94.7% and helps lower waste by 38.2%. With QAOA implementation, real-time adjustments can be done in variable-rate systems. This type of research aims to overcome important problems with current algorithms unable to work with high-dimensional datasets in agriculture. This work creates the base for new agriculture systems that help achieve high crop yields while keeping the environment safe through quick and effective methods of optimization.
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.
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 prediction of soil nutrient content is essential for precision agriculture and sustainable land management. Various soil properties, including nitrogen (N), Organic Carbon (OC), pH, phosphorus (P), and Electrical Conductivity (EC), exhibit complex oscillatory and quasi-periodic behaviors influenced by environmental cycles, moisture variation, and biological activity. In this study, we propose a novel Fourier Quantum Convolution (FQC) framework augmented with a quantum entangler circuit to extract robust spectral-domain features from soil spectral data. The proposed FQC Entangler Circuit processes local spectral patches through parameterized quantum gates and entangling operations, facilitating the extraction of amplitude and phase information while capturing inter-band correlations. The resulting Fourier quantum features serve as effective inputs to regression models for estimating soil nutrient content. The experimental results demonstrate that the FQC-transformed features significantly enhance the prediction accuracy of N, OC, pH, P, and EC compared to the conventional spectral and statistical features. This study underscores the potential of quantum-inspired feature extraction for advancing digital soil analysis and precision agriculture applications. For instance, on the phosphorus dataset, FQC achieved an RMSE of 3.89 and anR2of 0.178, outperforming other quantum circuits. Similarly, for the pH dataset, FQC yielded the lowest RMSE (1.16) and MAPE (13.04%), indicating superior generalization and predictive accuracy.
Modern agriculture generates large, heterogeneous and high-dimensional data streams whose volume and combinatorial complexity increasingly strain classical computation. Quantum and quantum-inspired computing have been proposed as a means of representing and searching such spaces more efficiently. This review synthesises the emergence of quantum-inspired artificial intelligence and computing in agriculture, maps where these methods have been applied, appraises the certainty of the reported evidence, and evaluates the conditions under which the methods are likely to add value over classical approaches. Following the PRISMA 2020 guideline, four bibliographic databases (Scopus, Web of Science, IEEE Xplore and PubMed) were searched for peer-reviewed studies published between 2017 and 2026, supplemented by citation chaining and a constrained grey-literature scan from which preprints reporting a fully specified method and result were also eligible. After removal of 149 duplicates, 478 records were screened on title and abstract, 82 were assessed in full text, and 17 studies met the eligibility criteria and were carried into a thematic, task-oriented synthesis; no quantitative meta-analysis was performed because reported outcomes are not commensurable. The 17 included studies concentrate in five domains including the quantum-inspired optimisation (2 studies), quantum machine learning (5), plant disease detection (3), genomics and bioinformatics (6), and precision-agriculture sensing (1). By task, the corpus clusters on classification (7 studies) and sequence assembly/alignment (6), with optimisation (2), clustering (1) and sensing (1) thinly represented, and prediction/regression and dynamic, time-resolved phenotyping absent (0 studies each). Reported gains are frequently large in headline terms. For example, quantum image-descriptor disease classifiers reporting accuracies near 99% on curated leaf datasets but rest predominantly on simulators or noisy intermediate-scale quantum (NISQ) hardware, curated benchmarks and weak classical baselines, so the certainty of any quantum advantage is low. Quantum-inspired methods are a credible near-term toolset that can run on classical hardware while quantum processors mature, but current evidence is simulation-heavy, weakly baselined and difficult to reproduce. The clearest research gap is dynamic disease and phenotype-progression modelling. The paper outline a roadmap toward Agriculture 5.0, quantum-enabled breeding and quantum phenomics that prioritises temporal, uncertainty-aware modelling supported by stronger baselines and field validation.
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.