Agriculture is increasingly threatened by diverse global security challenges, including climate change, biodiversity loss, and health security risks, underscoring the need for sustainable practices to ensure food security for future generations. Advanced technologies such as quantum machine learning (QML) offer transformative potential in addressing these challenges by enhancing crop disease monitoring systems. This paper explores the implementation of QML in agriculture, with a particular focus on its application to crop disease detection and prevention. Leveraging image processing, sensory data analysis, and spectral imaging, QML algorithms demonstrate superior accuracy and efficiency in early disease recognition, surpassing traditional methods. A case study of Hungary's crop disease monitoring system illustrates the practical benefits of integrating QML with technologies such as remote sensing and IoT. By enabling real-time monitoring and predictive analytics, QML not only aids in mitigating crop losses but also contributes to sustainable agricultural practices and global food security. Despite current limitations in quantum hardware, advancements in QML present promising opportunities for revolutionizing agricultural systems and ensuring resilience against evolving threats.
Agriculture is increasingly threatened by diverse global security challenges, including climate change, biodiversity loss, and health security risks, underscoring the need for sustainable practices to ensure food security for future generations. Advanced technologies such as quantum machine learning (QML) offer transformative potential in addressing these challenges by enhancing crop disease monitoring systems. This paper explores the implementation of QML in agriculture, with a particular focus on its application to crop disease detection and prevention. Leveraging image processing, sensory data analysis, and spectral imaging, QML algorithms demonstrate superior accuracy and efficiency in early disease recognition, surpassing traditional methods. A case study of Hungary's crop disease monitoring system illustrates the practical benefits of integrating QML with technologies such as remote sensing and IoT. By enabling real-time monitoring and predictive analytics, QML not only aids in mitigating crop losses but also contributes to sustainable agricultural practices and global food security. Despite current limitations in quantum hardware, advancements in QML present promising opportunities for revolutionizing agricultural systems and ensuring resilience against evolving threats.
Agriculture faces increasing risks in ensuring global food security, with challenges such as climate change, biodiversity loss, and plant disease and pest outbreaks posing significant threats to crop yields. These factors are compounded by growing global populations, resource limitations, and market fluctuations. Traditional methods for crop pest and disease monitoring, often reliant on manual surveys and basic machine learning models, are inadequate in addressing these challenges due to limited scalability and accuracy. This research explores the potential application of quantum machine learning (QML) for crop pest and disease monitoring, leveraging the advanced computational capabilities of quantum computers to improve detection accuracy, efficiency, and scalability. The study reviews existing monitoring systems, recent advancements in quantum computing technologies, and the integration of QML algorithms, which offer promising solutions for handling large, complex agricultural datasets. While the practical application of QML in agriculture is still in its early stages, its theoretical advantages indicate significant potential for future smart agricultural systems.
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 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.
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.
This paper presents a novel approach to crop disease classification through the integration of quantum computing principles with deep learning techniques. We introduce a hybrid quantum neural network architecture that leverages quantum mechanical phenomena, including superposition and entanglement, to enhance crop disease detection capabilities. Using a comprehensive dataset of 23,265 images spanning 19 disease classes across five major crop species, our model achieved a test accuracy of 0.7958 with a weighted average F1-score of 0.85. The architecture combines classical convolutional layers for initial feature extraction with quantum processing units for advanced pattern recognition, demonstrating significant potential in the diagnosis of agricultural diseases. Our findings establish the viability of quantum-enhanced neural networks in agricultural applications, while highlighting pathways for future development in quantum machine learning systems.
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.