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.
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.
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.
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.
This chapter explores the potential of quantum computing in agriculture, highlighting its potential to enhance efficiency, sustainability, and environmental stewardship. Quantum computing can process vast datasets, simulate complex systems, and optimize resource allocation. Applications include crop optimization, weather modeling, and precision agriculture. Case studies show quantum-assisted solutions for crop yield prediction, pest and disease management, and soil analysis, demonstrating the benefits of quantum computing in real-world agricultural scenarios. The integration of quantum computing in agriculture faces challenges such as technology accessibility, data management, energy consumption, and privacy concerns. However, the fusion of quantum technology and agriculture holds potential for global food security, environmental sustainability, and resource-efficient farming.
The innovations of quantum computing, sophisticated tools, and artificial intelligence (AI) are changing the future of sustainable farming to present solutions to resource management, precise care, and environment monitoring of crops and animals. This section talks about how the application of quantum computing transforms agricultural data analysis, optimization, and simulation processes. It highlights that the integration of the IoT sensors, quantum-enhanced AI system models, and heterogeneous modeling methods makes it manageable and automates processes in the farms. The second portion of the chapter discusses the application of AI in crop prediction, identification of diseases and pests, examination of livestock DNA, and irrigation systems management. Despite some remaining hardware and integration problems, quantum–classical and multi-disciplinary science cooperation/collaboration will most likely lead to greater scalability, efficiency, and sustainability in agriculture. Adopting the most recent technologies in the agricultural production industry can increase production, reduce damage to the environment, and guarantee the availability of food as the global demands evolve.
Quantum computing, an emerging paradigm in computational technology, holds the potential to revolutionize agricultural optimization by solving complex problems that are currently infeasible for classical computers. This chapter explores the application of quantum computing in key areas of agriculture, including crop yield optimization, climate modeling, supply chain efficiency, and resource allocation. By leveraging quantum algorithms such as Quantum Annealing, Variational Quantum Eigensolvers (VQE), and Quantum Machine Learning, agricultural processes can be optimized to enhance productivity, reduce waste, and minimize environmental impact. The integration of quantum computing with existing technologies like the Internet of Things (IoT) and artificial intelligence (AI) promises to accelerate the transition towards smart farming, enabling predictive analytics and real-time decision-making on an unprecedented scale.