Nigerian agriculture employs over 70 percent of rural populations and contributes substantially to national gross domestic product yet confronts mounting challenges from climate variability, resource constraints, and technological inadequacies threatening food security.Current crop yield prediction methodologies demonstrate critical limitations in accuracy, spatial resolution, and processing speed that hamper effective agricultural decision making.Classical computational approaches achieve prediction accuracies rarely exceeding 70 to 80 percent while requiring extended processing times that often render forecasts obsolete for practical farming applications.This research introduces QSCYPS (Quantum Enhanced Space Based Crop Yield Prediction System), integrating quantum computing architectures with satellite remote sensing across Nigerian agro ecological zones.The system employs three quantum algorithms: Quantum Fourier Transform for spectral feature extraction achieving O((log n)²) computational complexity versus classical O(n log n), Quantum Support Vector Machine for crop stress classification reaching 89 percent accuracy in simulation, and Quantum Approximate Optimization Algorithm for parameter calibration of DSSAT crop growth models.Theoretical analysis projects 42 percent reduction in root mean square error from 0.78 tonnes per hectare (classical hybrid baseline) to 0.45 tonnes per hectare for maize predictions, with coefficient of determination improving from 0.86 to 0.94.Computational scaling analysis demonstrates sixteen fold speedup at one million pixel processing scale, with advantages growing at national coverage scales exceeding 280 million pixels.The framework integrates Sentinel 2 imagery at ten metre resolution, ERA5 reanalysis climate data, and ISRIC SoilGrids parameters through Bayesian ensemble combining quantum extracted features with physics based crop models.While projections remain simulation based pending access to actual quantum hardware (IBM 127 qubit Eagle or Google Cirq platforms), QSCYPS establishes theoretical architecture and algorithmic foundation for operational quantum enhanced agricultural intelligence.This represents first integration of quantum information processing with satellite agricultural monitoring, positioning Nigerian institutions at forefront of quantum age precision agriculture.
Nigerian agriculture employs over 70 percent of rural populations and contributes substantially to national gross domestic product yet confronts mounting challenges from climate variability, resource constraints, and technological inadequacies threatening food security.Current crop yield prediction methodologies demonstrate critical limitations in accuracy, spatial resolution, and processing speed that hamper effective agricultural decision making.Classical computational approaches achieve prediction accuracies rarely exceeding 70 to 80 percent while requiring extended processing times that often render forecasts obsolete for practical farming applications.This research introduces QSCYPS (Quantum Enhanced Space Based Crop Yield Prediction System), integrating quantum computing architectures with satellite remote sensing across Nigerian agro ecological zones.The system employs three quantum algorithms: Quantum Fourier Transform for spectral feature extraction achieving O((log n)²) computational complexity versus classical O(n log n), Quantum Support Vector Machine for crop stress classification reaching 89 percent accuracy in simulation, and Quantum Approximate Optimization Algorithm for parameter calibration of DSSAT crop growth models.Theoretical analysis projects 42 percent reduction in root mean square error from 0.78 tonnes per hectare (classical hybrid baseline) to 0.45 tonnes per hectare for maize predictions, with coefficient of determination improving from 0.86 to 0.94.Computational scaling analysis demonstrates sixteen fold speedup at one million pixel processing scale, with advantages growing at national coverage scales exceeding 280 million pixels.The framework integrates Sentinel 2 imagery at ten metre resolution, ERA5 reanalysis climate data, and ISRIC SoilGrids parameters through Bayesian ensemble combining quantum extracted features with physics based crop models.While projections remain simulation based pending access to actual quantum hardware (IBM 127 qubit Eagle or Google Cirq platforms), QSCYPS establishes theoretical architecture and algorithmic foundation for operational quantum enhanced agricultural intelligence.This represents first integration of quantum information processing with satellite agricultural monitoring, positioning Nigerian institutions at forefront of quantum age precision agriculture.
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.
Farming is a prerequisite of the existence of human beings, but the agricultural sector is still subject to chronic issues, including pests, illnesses, soil erosion, water scarcity, and the consequences of the climate change.To cope with these challenges, the smart solutions to them are necessary, which will optimize the efficient use of land, water, and energy and will, at the same time, improve the resiliency and productivity of crops and soil.Remote sensing is important in the measurement of crops and soil parameters, such as temperature, moisture content, nutrient, stress, and disease, but the traditional types of remote sensing e.g.satellite imagery and aerial imagery have constraints of low resolution, high noise, high data and energy requirements, high costs and the complexity of remote sensing.In order to reduce these constraints, quantum computing is proposed to enhance quantum compressive ghost imaging using quantum metrology.It combines quantum enhanced sensing, that minimizes the need of measurements, and energy costs, with quantum enhanced estimation, that enhances the level of precision and accuracy using quantum light sources and quantum interferometry and entangled photons.Compared to conventional approaches, quantum computing enhanced quantum compressive ghost imaging has better resolution, lower noise, and lower cost as well as new capabilities and applications that cannot be established by classical means.The paper also addresses time and space complexity, applicability and challenges of quantum computing enhanced quantum compressive ghost imaging in agricultural practices with the help of algorithm design, testing protocols, experimental findings, and graphical illustrations.The results show that quantum computing quantum compressive ghost imaging is much better at estimating crop and soil parameters, which leads to sustainability and productivity in agricultural 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.
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.
Abstract: The increasing global demand for food, coupled with the challenges of climate change, resource scarcity, and supply chain inefficiencies, necessitates transformative solutions in agriculture and food security. Traditional AI-driven agricultural systems, while effective, often struggle with processing vast, complex, and interdependent variables such as weather patterns, soil quality, pest outbreaks, and logistics optimization. Quantum AI, which merges the computational power of quantum computing and artificial intelligence, offers unprecedented capabilities in predictive analytics, real-time decision-making, and optimization for sustainable farming. This chapter explores Quantum AI’s applications in precision agriculture, climate-resilient farming, pest and disease prediction, and smart food supply chains, demonstrating how quantum-enhanced models optimize irrigation, enhance crop yield forecasts, and improve food distribution efficiency. Additionally, it highlights the challenges, ethical considerations, and future potential of Quantum AI in ensuring global food security while promoting environmental sustainability. By integrating Quantum AI with IoT, blockchain, and remote sensing technologies, this chapter outlines a strategic roadmap for transforming agriculture into a highly efficient, adaptive, and climate-resilient system, paving the way for a food-secure and ecologically sustainable future. Keywords: Quantum AI, food security, sustainable agriculture, precision farming, climate-resilient farming, quantum computing in agriculture, smart food supply chains, predictive analytics, crop yield optimization, pest and disease prediction, quantum-enhanced logistics, irrigation management, AI-driven agriculture, blockchain for food traceability, sustainable resource management, quantum optimization algorithms