The conventional plant breeding has had some limitation in addressing the issues of global food security and challenges. Quantum computing proposes the way of improvement of genetic algorithm based optimization in plant breeding. The study attempts to analyse the impact of quantum computing in the acceleration of plant breeding. The chapter likewise examines the use of quantum algorithms and methods to anticipate complicated traits utilising genome selection and trait mapping. The research also focuses on quantum computing in order to find better gene combination, optimization-based breeding and crop resiliency in the face of the climate changes. Certain areas specifically include quantum acceleration, genome-wide association studies (GWAS) and quantum machine learning for phenotyping prediction and quantum inspired optimization algorithms to design novel breeding schemes. Such perspective paves way for new era to concentrate on improvement of crop and sustainable food production. Such innovation offers something to solve the global food security challenge in the 21st century.
The conventional plant breeding has had some limitation in addressing the issues of global food security and challenges. Quantum computing proposes the way of improvement of genetic algorithm based optimization in plant breeding. The study attempts to analyse the impact of quantum computing in the acceleration of plant breeding. The chapter likewise examines the use of quantum algorithms and methods to anticipate complicated traits utilising genome selection and trait mapping. The research also focuses on quantum computing in order to find better gene combination, optimization-based breeding and crop resiliency in the face of the climate changes. Certain areas specifically include quantum acceleration, genome-wide association studies (GWAS) and quantum machine learning for phenotyping prediction and quantum inspired optimization algorithms to design novel breeding schemes. Such perspective paves way for new era to concentrate on improvement of crop and sustainable food production. Such innovation offers something to solve the global food security challenge in the 21st century.
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.
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
Global challenges of food security, population growth, climate change and resource scarcity require increasingly urgent solutions. The emergence of quantum computing opens new opportunities for future agriculture, especially within the framework of the agriculture 5.0 vision. The study demonstrates how quantum algorithms and quantum simulations can be applied in key areas such as energy transfer in photosynthesis, increasing the efficiency of gene editing, designing protein structures, optimizing fertilizer production, and adapting to climate change. Through these applications, quantum technology can not only complement classical approaches in agri-informatics, but also open new dimensions towards sustainable, data-driven and intelligent food production. The aim of the thesis is to establish a functional framework that systematically maps the opportunities and benefits of integrating quantum technology into agriculture.
Molecular farming represents a revolutionary approach to producing pharmaceuticals, vaccines, and industrial proteins using plants and plant cell cultures as biofactories. The integration of quantum computing into molecular farming research promises to address computational bottlenecks in protein engineering, metabolic pathway optimization, and crop improvement. This paper explores the emerging applications of quantum computing in molecular farming, examining how quantum algorithms can accelerate drug discovery, optimize protein folding predictions, and enhance synthetic biology approaches. We discuss the current state of quantum computing technologies, their specific applications in plant-based biomanufacturing, and the challenges that must be overcome for widespread implementation. The convergence of quantum computing and molecular farming has the potential to transform biopharmaceutical production, offering more efficient, cost-effective, and environmentally sustainable alternatives to traditional manufacturing methods.
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.
Abstract Quantum computing (QC) is not a futuristic notion in agriculture, though its full potential has yet to be realized. QC is an emerging field at the intersection of physics and computer science that holds immense potential to revolutionize various sectors, including agriculture production and artificial intelligence (AI) modeling. While QC is still in the early stages of development and practical applications within agriculture are not yet widespread, researchers are actively exploring its potential benefits in various agricultural domains, including crop optimization, livestock breeding, and environmental monitoring. QC harnesses the principles of quantum mechanics to perform computations using quantum bits or qubits, which can exist in multiple states simultaneously. Unlike classical computers, which rely on binary bits representing 0 or 1, quantum computers exploit phenomena such as superposition and entanglement to process information in parallel, potentially offering exponential speedup for certain types of problems. In agriculture production, particularly in animal science, QC offers promising avenues for optimizing processes and enhancing productivity. Quantum algorithms can analyze vast amounts of genomic data to improve breeding programs, leading to the development of more resilient and productive livestock breeds. Furthermore, QC can facilitate precision farming techniques by modeling complex environmental factors and animal behavior to optimize feeding strategies, disease management, and overall farm management practices. Moreover, QC can significantly benefit AI modeling by accelerating computations and enabling more efficient training of AI models. Quantum algorithms can enhance the performance of AI algorithms in various tasks, including pattern recognition, natural language processing, and predictive analytics. By leveraging quantum-enhanced optimization algorithms, AI models can achieve better convergence and accuracy, leading to more effective decision-making and problem-solving capabilities. While hybrid intelligent models also represent a novel frontier in agriculture, QC has the potential to expedite the merging of mechanistic and AI modeling paradigms, facilitating a more holistic understanding of complex systems in agriculture and beyond. By integrating mechanistic models, which describe the underlying physical processes, with AI models, which learn patterns from data, quantum computing can enable comprehensive simulations and predictions of agricultural systems. This fusion of modeling paradigms can lead to more accurate and robust predictions of crop yields, livestock performance, and environmental impacts, facilitating informed decision-making for farmers and policymakers. The application of QC in agriculture, however, requires interdisciplinary collaborations between physicists, computer scientists, agronomists/animal scientists, and AI researchers. These collaborations can drive the development of quantum algorithms tailored to agricultural applications, the integration of quantum-enhanced AI techniques into existing modeling frameworks, and the deployment of QC resources in real-world agricultural systems. Ultimately, harnessing the power of QC holds the potential to revolutionize agriculture production practices, including regenerative agriculture, and advance AI modeling capabilities, paving the way for a more sustainable and efficient agricultural industry.