Precision agriculture generates increasingly complex datasets from sensors, satellite imagery, and IoT devices, creating computational challenges for conventional machine learning and optimization approaches. Quantum computing has emerged as a potential alternative for addressing high-dimensional and nonlinear problems through quantum algorithms. Although initial applications have been reported in areas such as crop classification, prediction, and resource optimization, a systematic assessment of these approaches in agriculture is still lacking. This review aims to synthesize current applications, identify methodological trends and limitations, and highlight future research opportunities in quantum computing for agriculture.
Precision agriculture generates increasingly complex datasets from sensors, satellite imagery, and IoT devices, creating computational challenges for conventional machine learning and optimization approaches. Quantum computing has emerged as a potential alternative for addressing high-dimensional and nonlinear problems through quantum algorithms. Although initial applications have been reported in areas such as crop classification, prediction, and resource optimization, a systematic assessment of these approaches in agriculture is still lacking. This review aims to synthesize current applications, identify methodological trends and limitations, and highlight future research opportunities in quantum computing for agriculture.
Precision agriculture generates increasingly complex datasets from sensors, satellite imagery, and IoT devices, creating computational challenges for conventional machine learning and optimization approaches. Quantum computing has emerged as a potential alternative for addressing high-dimensional and nonlinear problems through quantum algorithms. Although initial applications have been reported in areas such as crop classification, prediction, and resource optimization, a systematic assessment of these approaches in agriculture is still lacking. This review aims to synthesize current applications, identify methodological trends and limitations, and highlight future research opportunities in quantum computing for agriculture.
With increasing computational demands in agriculture and life sciences, quantum computing is emerging as a potential alternative to classical computing. Unlike classical computers, which utilize binary bits, quantum computers utilize quantum bits (qubits) with unique properties such as superposition and entanglement, enabling them to solve certain computational problems more efficiently and achieve significant speed-ups in specific applications. In this manuscript, we evaluate the potential of quantum computing in agriculture and life sciences by reviewing computational challenges suitable for quantum computing and exploring exemplary domain applications. We examine optimization problems in agrifood supply chains, large-scale linear equation systems in animal breeding, quantum-based network architectures for machine learning in classifying satellite images for land-use analysis, quantum simulations for resource recovery from agriculture waste streams, and quantum search algorithms for genome assembly. Each computational problem type presents unique opportunities and challenges, underscoring the need for tailored quantum algorithms. Furthermore, we provide a critical assessment of the broader potential of quantum computing, discussing its challenges, limitations, and how to facilitate a potential implementation. While current quantum hardware remains limited, developing quantum algorithms is still valuable — not only to prepare for future advancements but also to foster innovation through interdisciplinary collaboration. Rather than replacing traditional computing, we foresee quantum computing complementing classical systems, offering novel solutions to previously intractable problems. Continued research and interdisciplinary collaborations are essential to realize the full potential of quantum computing, paving the way for pioneering advancements in agriculture and life sciences.
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.
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.
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.
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.