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.
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.
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 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.
Smart agriculture has emerged as a transformative approach to enhance crop productivity, resource efficiency, and environmental sustainability using advanced sensing and computational technologies. Wireless Sensor Networks (WSNs) enable real-time monitoring of agricultural parameters such as soil moisture, temperature, humidity, and crop health. However, traditional machine learning models struggle to efficiently process high-dimensional, noisy, and heterogeneous agricultural sensor data. This study proposes a Wireless Sensor-Driven Smart Agriculture framework using Scalable Quantum Convolutional Models (SQCM). The proposed model integrates wireless sensor data acquisition with quantum-inspired convolutional neural networks to enhance pattern recognition, predictive accuracy, and scalability. The quantum convolutional layers enable high-dimensional feature representation, while classical layers ensure efficient decision-making for agricultural optimization. The model is evaluated using agricultural sensor datasets, and performance is measured using accuracy, precision, recall, and RMSE for environmental prediction tasks. Experimental results demonstrate that the proposed quantum-enhanced architecture outperforms traditional machine learning and deep learning models in crop condition prediction and resource optimization. The framework is suitable for precision agriculture, automated irrigation systems, and smart farming applications.
Crop yield forecasting is critical for agricultural planning, resource distribution, and global food security. Nevertheless, the available literature is largely based on classical machine learning models or isolated simulation methods, which are not always effective at modeling the intricate non-linear processes between climatic variability, soil characteristics, and environmental conditions. To overcome these shortcomings, this study presents a superior predictive model, Entanglement-Aware Variational Quantum Ensemble Regression with Uncertainty-Aware Ensemble Weighting (EA-UAQER). The given approach will combine reference to quantum-inspired ensemble learning and uncertainty-sensitive weighting schemes to improve predictive generalization, stability, and resilience to heterogeneous data regimes. An experimental assessment was conducted using a publicly available Kaggle crop yield dataset with a variety of soil and meteorological characteristics. Computational libraries were developed in Python to train, validate, and assess model performance. The EA-UAQER model produced better results with an Root Mean Squared Error (RMSE) of 0.124, Mean Absolute Error (MAE) of 0.089, R 2 of 0.985, and finally Mean Absolute Percentage Error (MAPE) of 1.84% as compared to other traditional benchmark models such as the Random Forest, Support Vector Machine, and Artificial Neural Network. These findings indicate that the framework can model complex non-linear relationships with lower prediction variance and uncertainty. Through the integration of quantum-inspired optimization with ensemble intelligence, the project will provide a scalable, robust solution for next-generation intelligent agricultural forecasting systems to enable data-driven decision-making and sustainable crop management and food production strategies.
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.