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.
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.
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.
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.
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.
Abstract. The legalization of medical cannabis in Morocco’s northern Rif region requires precision agriculture systems capable of supporting highly controlled, traceable and quality-driven cultivation. Medical cannabis is biologically sensitive to micro-variations in soil moisture, vapor pressure deficit (VPD), canopy temperature and nutrient levels, which makes it a demanding testbed for advanced decision-support methods. In this work, we propose and numerically evaluate an end-to-end hybrid quantum–classical framework that combines IoT sensor networks, Sentinel-2 and UAV imagery, GIS integration and quantum-enhanced analytics for regulated medical cannabis cultivation in the Al-Hoceïma region. The framework instantiates three quantum modules: (i) a variational quantum linear solver (VQLS) for Kriging-based spatial interpolation under sparse sensing, (ii) a variational quantum classifier (VQC) for early stress detection from multi-source features, and (iii) a Quantum Approximate Optimization Algorithm (QAOA) for constrained irrigation scheduling. All experiments are conducted on synthetic yet agro-ecologically calibrated data generated for a 4-hectare virtual plot; no real cannabis-field data or quantum hardware are used. In this controlled simulation setting, the quantum-inspired modules achieve moderate improvements over classical baselines (Kriging, Random Forest, neural networks, MILP), for example reducing interpolation RMSE by about 20% and improving early-stress F1-score by several percentage points. We explicitly do not claim hardware-level quantum advantage, nor do we provide a formal proof that VQLS or VQC must outper- form classical Kriging or machine learning in this regime. Instead, the contribution is a transparent formulation and simulation- based assessment of quantum-compatible workflows for precision agriculture in regulated contexts, together with a critical discus- sion of their current limitations and the conditions under which they might become competitive in practice.
Efficient irrigation scheduling is a major challenge in precision agriculture due to increasing water scarcity, climate variability, and growing agricultural demand. Conventional irrigation strategies based on fixed thresholds, heuristic rules, or classical optimization techniques often suffer from scalability limitations, inefficient resource allocation, and poor adaptability under dynamic environmental conditions. This paper presents a hybrid quantum-classical optimization framework for intelligent irrigation scheduling using the Quantum Approximate Optimization Algorithm (QAOA). The irrigation decision problem is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) model integrating soil moisture conditions, irrigation constraints, and water allocation objectives. The proposed framework employs variational quantum optimization to determine optimal irrigation decisions while minimizing unnecessary water consumption. Experimental evaluations conducted using Qiskit-based simulations demonstrate that the proposed QAOA framework successfully generates irrigation decisions fully consistent with classical threshold-based optimization for the evaluated dataset. Comprehensive visualization analyses including soil moisture distribution, irrigation decision mapping, threshold boundary analysis, and water utilization comparison validate the correctness and interpretability of the proposed optimization mechanism. Although the current experimental setup primarily serves as a proof-of-concept validation, the results indicate strong scalability potential for future large-scale agricultural systems involving complex multi-objective optimization constraints. The proposed framework establishes a foundational architecture for future quantum-enabled precision agriculture systems integrating IoT sensing, real-time environmental monitoring, and intelligent resource optimization for sustainable smart farming applications.