Modern agriculture generates large, heterogeneous and high-dimensional data streams whose volume and combinatorial complexity increasingly strain classical computation. Quantum and quantum-inspired computing have been proposed as a means of representing and searching such spaces more efficiently. This review synthesises the emergence of quantum-inspired artificial intelligence and computing in agriculture, maps where these methods have been applied, appraises the certainty of the reported evidence, and evaluates the conditions under which the methods are likely to add value over classical approaches. Following the PRISMA 2020 guideline, four bibliographic databases (Scopus, Web of Science, IEEE Xplore and PubMed) were searched for peer-reviewed studies published between 2017 and 2026, supplemented by citation chaining and a constrained grey-literature scan from which preprints reporting a fully specified method and result were also eligible. After removal of 149 duplicates, 478 records were screened on title and abstract, 82 were assessed in full text, and 17 studies met the eligibility criteria and were carried into a thematic, task-oriented synthesis; no quantitative meta-analysis was performed because reported outcomes are not commensurable. The 17 included studies concentrate in five domains including the quantum-inspired optimisation (2 studies), quantum machine learning (5), plant disease detection (3), genomics and bioinformatics (6), and precision-agriculture sensing (1). By task, the corpus clusters on classification (7 studies) and sequence assembly/alignment (6), with optimisation (2), clustering (1) and sensing (1) thinly represented, and prediction/regression and dynamic, time-resolved phenotyping absent (0 studies each). Reported gains are frequently large in headline terms. For example, quantum image-descriptor disease classifiers reporting accuracies near 99% on curated leaf datasets but rest predominantly on simulators or noisy intermediate-scale quantum (NISQ) hardware, curated benchmarks and weak classical baselines, so the certainty of any quantum advantage is low. Quantum-inspired methods are a credible near-term toolset that can run on classical hardware while quantum processors mature, but current evidence is simulation-heavy, weakly baselined and difficult to reproduce. The clearest research gap is dynamic disease and phenotype-progression modelling. The paper outline a roadmap toward Agriculture 5.0, quantum-enabled breeding and quantum phenomics that prioritises temporal, uncertainty-aware modelling supported by stronger baselines and field validation.
Modern agriculture generates large, heterogeneous and high-dimensional data streams whose volume and combinatorial complexity increasingly strain classical computation. Quantum and quantum-inspired computing have been proposed as a means of representing and searching such spaces more efficiently. This review synthesises the emergence of quantum-inspired artificial intelligence and computing in agriculture, maps where these methods have been applied, appraises the certainty of the reported evidence, and evaluates the conditions under which the methods are likely to add value over classical approaches. Following the PRISMA 2020 guideline, four bibliographic databases (Scopus, Web of Science, IEEE Xplore and PubMed) were searched for peer-reviewed studies published between 2017 and 2026, supplemented by citation chaining and a constrained grey-literature scan from which preprints reporting a fully specified method and result were also eligible. After removal of 149 duplicates, 478 records were screened on title and abstract, 82 were assessed in full text, and 17 studies met the eligibility criteria and were carried into a thematic, task-oriented synthesis; no quantitative meta-analysis was performed because reported outcomes are not commensurable. The 17 included studies concentrate in five domains including the quantum-inspired optimisation (2 studies), quantum machine learning (5), plant disease detection (3), genomics and bioinformatics (6), and precision-agriculture sensing (1). By task, the corpus clusters on classification (7 studies) and sequence assembly/alignment (6), with optimisation (2), clustering (1) and sensing (1) thinly represented, and prediction/regression and dynamic, time-resolved phenotyping absent (0 studies each). Reported gains are frequently large in headline terms. For example, quantum image-descriptor disease classifiers reporting accuracies near 99% on curated leaf datasets but rest predominantly on simulators or noisy intermediate-scale quantum (NISQ) hardware, curated benchmarks and weak classical baselines, so the certainty of any quantum advantage is low. Quantum-inspired methods are a credible near-term toolset that can run on classical hardware while quantum processors mature, but current evidence is simulation-heavy, weakly baselined and difficult to reproduce. The clearest research gap is dynamic disease and phenotype-progression modelling. The paper outline a roadmap toward Agriculture 5.0, quantum-enabled breeding and quantum phenomics that prioritises temporal, uncertainty-aware modelling supported by stronger baselines and field validation.
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.
This study investigates the effectiveness of a hybrid quantum-classical framework for plant disease detection, with a focus on improving classification accuracy compared to conventional approaches. Image-based datasets of crop diseases were processed and encoded into variational quantum circuits (VQCs), which were then integrated with classical machine learning classifiers. The proposed model achieved an overall classification accuracy of 95.2%, outperforming a baseline convolutional neural network that recorded 91.4% accuracy on the same dataset. Performance evaluation was carried out using multiple metrics including precision, recall, and F1-score, with the hybrid approach showing consistent gains across all categories. Furthermore, the model demonstrated reduced overfitting and improved robustness when tested under noisy and high-dimensional input conditions. These results highlight the potential of quantum-assisted machine learning to significantly enhance crop disease detection, offering a scalable and accurate pathway for sustainable agriculture and precision farming.
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.
Abstract Animal nutrition and agricultural systems are defined by complex, interconnected challenges involving biological nonlinearity, uncertainty, highdimensional data, and competing objectives. Although classical computing, statistical modeling, and artificial intelligence have enabled significant advances, many core problems, such as largescale optimization, multivariate prediction, and the integration of heterogeneous biological and production data, become difficult to address as problems scale in size and complexity. Quantum computing is an emerging computational paradigm that leverages quantum mechanical principles to explore solution spaces in fundamentally different ways than classical systems. While quantum hardware is still in an early stage of development, hybrid quantum-classical approaches are beginning to reveal potential applications relevant to both animal nutrition and agriculture, particularly in areas that combinatorial complexity, uncertainty, or computational intractability limit conventional methods. Beyond its theoretical appeal, quantum computing is attracting attention because many agricultural systems problems are inherently combinatorial, dynamic, and multi-objective. Animal nutrition, for example, often requires balancing biological responses, economic constraints, ingredient variability, and environmental outcomes within the same decision framework. These characteristics make the field a useful case study for discussing where quantum-inspired or hybrid quantum-classical methods may eventually offer practical value. This presentation explores quantum computing and what makes it fundamentally different from traditional computing. Key approaches and modalities are introduced, along with their strengths and weaknesses. The talk concludes by examining specific potential uses cases in agriculture and animal nutrition, including ration formulation, resource allocation, systems modeling, and decision support under uncertainty, and how quantum computing may provide an advantage as these fields move toward increasingly data-rich and computationally demanding problems.
Quantum Computing (QC) and Artificial Intelligence (AI) have emerged as key technologies in the evolution of Industry 6.0, driving advancements in automation and advanced analytics, and process optimization. Their integration holds the potential to revolutionize sectors such as data science, healthcare, finance, and cybersecurity by enabling faster and more efficient computations through qubits, superposition, and quantum entanglement. However, the lack of structured knowledge regarding specific QC methodologies and applications in AI hinders its optimal implementation and development. Consequently, this study aims to identify the applications and variables associated with QC-AI integration. To this end, a systematic literature review was conducted following the PRISMA 2020 methodology, drawing on studies from Scopus and Web of Science databases. This enabled the analysis of trends, limitations, and opportunities in this technological convergence. This study aims to systematically examine the intersection of quantum computing and artificial intelligence by identifying the key technological features, integration requirements, and sectoral applications that define the current state of the field. The review contributes by mapping existing research, highlighting methodological approaches, and revealing gaps that may guide targeted advancements in hybrid quantum AI systems. The insights generated have the potential to accelerate innovation in high-impact domains such as healthcare, finance, energy, and cybersecurity. The findings indicate that the main advances in QC applied to AI focus on quantum optimization, Quantum Machine Learning (QML), and post-quantum cryptography. Notably, sectors such as energy, healthcare, and finance have shown significant progress in adopting these technologies. For example, in healthcare, QML has been applied to simulate molecular interactions to accelerate drug discovery, and in finance, it enhances predictive models for market behavior. The study concludes that although QC demonstrates substantial potential to enhance AI, its broader adoption remains constrained by reliance on NISQ hardware, the need for effective error correction, and the limited scalability of hybrid quantum classical algorithms. Addressing these challenges will be essential to establishing QML as a cornerstone of technological innovation and digital transformation. Additionally, this review introduces an integrative framework that categorizes key AI QC convergence dimensions and proposes a classification of application areas based on technical requirements and algorithmic capabilities. These contributions aim to guide future experimental validations and hybrid model development.
Quantum and quantum-inspired image segmentation lies at the intersection of quantum computing, computer vision, machine learning and medical image analysis. We map the field from 422 Scopus-indexed publications (2018–2025) retrieved with a documented Advanced Search query and screened in three explicit levels. Formal eligibility retained 408 records. A published topical decision rule was then applied in two operationalizations that differ only in the metadata scope they read (observed agreement 82.8%, Cohen’s κ = 0.65); all 197 records in the two agreement-relevant strata were adjudicated at record level, and the 211 records both operationalizations excluded were validated on a random sample of 60. The final topical corpus contains 143 records, and at the sample rate roughly two to three further eligible records remain in the unexamined remainder. Screening changes the headline picture: the retrieved corpus grows at 20.45% a year (28 records in 2018 to 103 in 2025) but the topical corpus at only 10.41% (13 to 26), so about half of the apparent expansion is peripheral retrieval. China, India and the United States lead at every level. Of the 138 method studies, 9 (6.5%) report execution on physical quantum hardware, 43 (31.2%) are gate-based or variational studies run on simulators or with an unstated environment, 12 (8.7%) use annealing, Ising or QUBO formulations, and 74 (53.6%) are quantum-inspired classical algorithms that require no quantum device; 27 (19.6%) combine a classical vision pipeline with a quantum component. Quantum-inspired classical work falls from 78.0% to 29.2% of the period’s method studies while hybrid work rises from 3 to 19 records. All findings describe publication patterns, not segmentation performance or quantum advantage. A versioned data and code package reproduces every reported number.