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Journal of Cloud Computing Advances Systems and Applications

Optimizing resource allocation in precision farming using quantum enhanced algorithms and quantum sensor networks

Hatoon S. AlSagri, Ankit Kumar, Abdul Khader Jilani Saudagar, Abhishek Kumar, Linesh Raja

Published Sep 24, 2025• 13 citations
Publisher

Abstract

This study proposes QYieldOpt, a hybrid quantum-classical framework for real-time resource optimization in precision farming, integrating a Quantum Approximate Optimization Algorithm (QAOA-R), Quantum Gradient Allocation Optimizer (QGAO), and quantum algorithm for Sensor Feedback Calibration (QSFC). All results presented in this study are based on simulation experiments using realistic agricultural data sets and quantum circuit emulators. Addressing the classical limitations in dynamic, multi-constraint agricultural environments, the system leverages quantum computing parallelism and ultra-sensitive environmental monitoring using quantum sensor networks (QSNs). QAOA-R solves discrete resource allocation (irrigation valve on/off decisions) via cost Hamiltonian optimization, achieving 89% water utilization and 8492 kg yield in the simulations. QGAO refines continuous variables (fertilizer dosage) using quantum-enhanced gradient descent, reducing resource waste by 30% using penalty-augmented utility functions. QSFC dynamically calibrates utility parameters $$\:{a}_{i},\:{b}_{i}$$ via quantum sensor data, encoding variables like soil moisture into rotation gates $$\:{R}_{y}$$ ( $$\:\pi\:{s}_{ij}$$ ) with < 2% spectral error. The closed-loop architecture of the framework enables adaptive adjustments every 15–30 min using real-time QSN feedback. Empirical validation conducted entirely through simulation against classical models (LP, GA, PSO, RL) demonstrated superior performance with 12–18% yield improvements, 22% resource savings, and 4.3 s convergence for 100-zone farms. Under 20% sensor noise, QYieldOpt maintained robustness (R² = 0.919), outperforming classical baselines in terms of accuracy (MAE: 5.41 kg/zone) and scalability (10.6 s for 250 zones). By unifying quantum optimization with high-precision sensing, this study advances sustainable agriculture through energy-efficient resource management, which was validated in simulated and hybrid emulated cloud-edge environments. The modular design ensures theoretical compatibility with existing IoT systems, whereas field trials are essential to establish the practical feasibility of climate-resilient farming. As quantum hardware matures, QYieldOpt paves the way for autonomous and scalable solutions to global food security challenges in the future.

DOI
10.1186/s13677-025-00769-2

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This study proposes QYieldOpt, a hybrid quantum-classical framework for real-time resource optimization in precision farming, integrating a Quantum Approximate Optimization Algorithm (QAOA-R), Quantum Gradient Allocation Optimizer (QGAO), and quantum algorithm for Sensor Feedback Calibration (QSFC). All results presented in this study are based on simulation experiments using realistic agricultural data sets and quantum circuit emulators. Addressing the classical limitations in dynamic, multi-constraint agricultural environments, the system leverages quantum computing parallelism and ultra-sensitive environmental monitoring using quantum sensor networks (QSNs). QAOA-R solves discrete resource allocation (irrigation valve on/off decisions) via cost Hamiltonian optimization, achieving 89% water utilization and 8492 kg yield in the simulations. QGAO refines continuous variables (fertilizer dosage) using quantum-enhanced gradient descent, reducing resource waste by 30% using penalty-augmented utility functions. QSFC dynamically calibrates utility parameters  ai, bi\:{a}_{i},\:{b}_{i}ai​,bi​ via quantum sensor data, encoding variables like soil moisture into rotation gates  Ry\:{R}_{y}Ry​ (  π sij\:\pi\:{s}_{ij}πsij​ ) with < 2% spectral error. The closed-loop architecture of the framework enables adaptive adjustments every 15–30 min using real-time QSN feedback. Empirical validation conducted entirely through simulation against classical models (LP, GA, PSO, RL) demonstrated superior performance with 12–18% yield improvements, 22% resource savings, and 4.3 s convergence for 100-zone farms. Under 20% sensor noise, QYieldOpt maintained robustness (R² = 0.919), outperforming classical baselines in terms of accuracy (MAE: 5.41 kg/zone) and scalability (10.6 s for 250 zones). By unifying quantum optimization with high-precision sensing, this study advances sustainable agriculture through energy-efficient resource management, which was validated in simulated and hybrid emulated cloud-edge environments. The modular design ensures theoretical compatibility with existing IoT systems, whereas field trials are essential to establish the practical feasibility of climate-resilient farming. As quantum hardware matures, QYieldOpt paves the way for autonomous and scalable solutions to global food security challenges in the future.
This paper presents a Quantum-Optimized Decision Support System (QoDSS) for IoT-driven precision irrigation, addressing critical challenges in water conservation and real-time agricultural monitoring. We propose a hybrid quantum-classical architecture combining soil moisture sensors with the Quantum Approximate Optimization Algorithm (QAOA) to optimize irrigation control. The system dynamically adjusts valve and pump operations based on real-time field data, achieving 61% higher decision accuracy (reducing error from 158% to 62%) compared to classical methods, 58% water savings (310L/day), and complete prevention of waterlogging scenarios (tested up to 95% moisture levels). Field experiments demonstrate reliable operation with 2.1s average decision latency, validated across extreme moisture conditions (10% drought to 95% saturation). Key innovations include: (1) A novel QUBO formulation for moisture optimization (α = 2.3, β = 1.7, γ = 3.0), (2) Edge-quantum integration (RPi4 + IBMQ 5-qubit backend), and (3) Automated anomaly correction. This work provides a practical framework for quantum-IoT solutions in agriculture, demonstrating 38% energy reduction and 95% system reliability in real-world conditions.
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.
Changes in Weather decrease in soil fertility, limited water resources and growing populations all are making agriculture harder. Even though precision agriculture uses traditional machine learning techniques, the majority of these approaches are unable to effectively handle the extremely complex and multidimensional data coming from crop genetics, weather systems, soil sensors, and satellite surveillance. In this regard, the current research presents a framework for intelligent agriculture that is based on quantum machine learning and quantum optimization. The system can represent and analyze the agricultural data far more efficiently than traditional computer methods by taking advantage of quantum phenomena like superposition and entanglement. Improved crop yield prediction and intelligent resource management via quantum-inspired optimization, particularly QAOA, are the two main objectives of the work. The model achieves accuracy values for yield estimates between 70 and 90%, according to simulation results, and it allows for real-time modification of fertilizer and irrigation. Lastly, it provides farmers with an easy-to-use dashboard to visualize insights. The current study provides a conceptual and technical foundation for quantum computing applications in agriculture in the near future, for more resource-efficient, data-driven, and sustainable farming, even though current quantum hardware is still in the development stage.
New zenith of agriculture technology, precision agriculture has emerged as a crucial tool to feed India and manage the scarce irrigation facility where nearly 600 million people are facing hardship of severe water crisis. This paper proposes a new algorithm of variational quantum computing (VQC), which has showed potential in optimizing crop yield and resource utilisation based on the data sets such as soil quality, climate and genetic makeup of crops. The algorithm makes use of qubits to allow the real time data processing through IoT sensors to allow for real time monitoring and decision making. Two trials performed on various agricultural data sets this approach presented about 30% increase in the predictive accuracy of crop yields and 25% decrease in water and fertilizer usage. Furthermore, enhanced detection capacities enhanced illness control capacities by 40% thereby leading to decreased crop losses. In addition to bridging significant gaps in the currently available literature, this paper incorporates quantum computing into precision agriculture to offer farmers and other stakeholders’ usable formulations that may shape the future of farming with the help of advancing quantum technologies.
New zenith of agriculture technology, precision agriculture has emerged as a crucial tool to feed India and manage the scarce irrigation facility where nearly 600 million people are facing hardship of severe water crisis. This paper proposes a new algorithm of variational quantum computing (VQC), which has showed potential in optimizing crop yield and resource utilisation based on the data sets such as soil quality, climate and genetic makeup of crops. The algorithm makes use of qubits to allow the real time data processing through IoT sensors to allow for real time monitoring and decision making. Two trials performed on various agricultural data sets this approach presented about 30% increase in the predictive accuracy of crop yields and 25% decrease in water and fertilizer usage. Furthermore, enhanced detection capacities enhanced illness control capacities by 40% thereby leading to decreased crop losses. In addition to bridging significant gaps in the currently available literature, this paper incorporates quantum computing into precision agriculture to offer farmers and other stakeholders’ usable formulations that may shape the future of farming with the help of advancing quantum technologies.
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.