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.
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.
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.
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
Quantum computing, artificial intelligence (AI), and robots are all coming together to make sustainable agriculture possible by making it possible to build advanced self-driving systems for precision farming. This paper looks at how quantum-enhanced AI might be able to make farming much more productive, use resources more efficiently, and protect the environment. In traditional farming, old techniques are often still used, which can waste resources and hurt the environment. By combining quantum computing with AI and robotics, we can use the power of quantum algorithms to process huge amounts of agricultural data faster than with traditional systems. This makes it possible to create smart decision-making tools that increase crop yields while lowering costs and damage to the environment. Quantum computing has unmatched computer power, which makes it possible to analyze large files from a variety of sources, such as earth monitors, weather forecasts, and satellite images. Because of this feature, complex prediction models can be made that can accurately predict how crops will do in different situations. The paper study used suggested model along with AI to handle important farming jobs like planting, watering, and gathering by using robots with advanced sensors and machine learning algorithms. With the use of self-driving robots in farming not only makes operations more efficient, but it also cuts down on the need for physical labor, which helps the agricultural sector deal with its lack of workers.
Globally due to growing population and climatic changes intensifies and ensures food security for sustainable agriculture. Traditional agriculture has several challenges like, resource allocation, precise monitoring and crop production. Quantum computing has opened up new avenue in the agricultural field. The potential use of quantum computing in transforming agriculture practices is to enhance sustainable crop management and yield, precision farming, optimizing resource and data security are set to revolutionize every aspect of farming. This innovation has bought a new perspective to integrate crop and livestock farming to high productivity, control pests and diseases and risks. The integration of quantum computing into agriculture is not only support for increasing yields and earnings rather it plays a key role in economic revolution and environmental friendly. This chapter explores the overview, optimization algorithms, applications and prospects of quantum computing for Sustainable 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.
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.