The new-type power system is rapidly evolving toward a stage characterized by high renewable-energy penetration, deep participation of diverse loads, and the coordinated digitalization of energy resources, while simultaneously facing significant challenges such as reduced system inertia, insufficient voltage support, and increasingly volatile and uncertain power outputs. Against this backdrop, artificial intelligence (AI), with its strengths in high-dimensional system representation, complex nonlinear correlation mining, and real-time optimal decision-making, is becoming a key technological driver across the full chain of power system sensing, forecasting, and regulation. This study provides a comprehensive overview of AI-based technological frameworks on the generation, grid, and load sides, covering machine learning, deep learning, reinforcement learning, and hybrid-intelligence paradigms, and systematically reviews recent advances in renewable-energy output forecasting, load behavior modeling, system situational awareness, economic dispatch, voltage and frequency control, disturbance response, and resilience enhancement. Building on these insights, the study further explores the potential value and emerging trends of digital-twin power grids, physics-informed neural networks (PINNs), explainable AI (XAI), and large model technologies in new-type power system regulation, while identifying key bottlenecks related to data quality and availability, model generalization, computational real-time performance, and engineering implementation. The goal is to establish a structured cognitive framework for AI-enabled power system regulation and to provide theoretical support and directional guidance for future technological innovation, engineering practices, and interdisciplinary integration.

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Against the backdrop of high-penetration renewable energy integration and the convergence of wide-area sensing and intelligent control, power systems are rapidly evolving into highly coupled cyber–physical systems, whose security challenges have expanded from traditional physical instability to systemic risks driven by cross-domain cyber–physical interactions. Compared with isolated information security or equipment reliability problems, cyber–physical power system threats exhibit multidimensional attack surfaces, cross-layer propagation, covert manipulation, and coordinated cyber–physical amplification, posing fundamental challenges to state awareness, control consistency, and system stability. This paper systematically reviews recent advances in cyber–physical power system security by summarizing the architectures, attack pathway evolution, and modeling paradigms of representative threats such as false data injection, industrial communication attacks, and coordinated cyber–physical attacks, and by comparatively analyzing attack detection, situational awareness, and risk assessment methods based on physical models, data-driven approaches, and cyber–physical fusion frameworks, together with their applicability and limitations in interpretability, real-time performance, and robustness. From a system-level perspective, it further examines progress in active defense, resilient control, and security-oriented resilience enhancement, highlights the shift from passive protection to adaptive and evolutionary resilience, and finally establishes a unified analytical framework centered on “threat modeling–key technologies–system resilience evolution,” while outlining future directions including AI-enabled autonomous defense, digital twin–driven security assessment, and resilience-oriented system design to support both theoretical research and engineering practice.

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Driven by the “dual carbon” goals, large-scale renewable integration is accelerating the transition of power systems from synchronous generator–dominated structures to power-electronics-dominated paradigms. As a result, system inertia, damping, and short-circuit capacity are significantly reduced, and operational characteristics are increasingly marked by low inertia, high power-electronic penetration, and strong uncertainty, fundamentally reshaping frequency, voltage, and rotor-angle stability mechanisms. To address these challenges, active support control technologies, such as grid-forming (GFM) converters and virtual synchronous generators (VSG), are rapidly developing and being integrated with flexibility resources, including energy storage, demand response, and virtual power plants, becoming key enablers of stability and resilience in new-type power systems. In parallel, coordinated operation of multi-energy systems, source–grid–load–storage integration, and cross-timescale optimal dispatch is evolving to support secure and economical renewable integration. However, major challenges remain, including converter-dominated stability, dynamic interactions under hybrid GFM and grid-following (GFL) operation, complex multi-energy coupled modeling, and quantitative valuation and market mechanisms for flexibility resources. This paper reviews recent advances from the perspectives of stability mechanisms, active support control, and coordinated optimization, and proposes a unified analytical framework for future power systems.

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Against the backdrop of high renewable penetration, extensive deployment of power electronic equipment, and rising grid resilience requirements, the operating mechanisms of new power systems are undergoing profound structural changes. Power transformers are increasingly exposed to wideband electromagnetic disturbances, multi-timescale coupled effects, and frequent extreme operating conditions, making conventional steady-state and power–frequency–based design and operation paradigms inadequate. This paper reviews recent advances in key transformer technologies, including insulation materials and structural optimization, thermo–electro–magnetic multi-physics modeling, electromagnetic performance enhancement, and adaptive design under complex operating conditions. Particular attention is given to multi-source online monitoring, hybrid mechanisms, and data-driven fault diagnosis, lifetime assessment, and intelligent operation and maintenance. A comprehensive evaluation is conducted in terms of operational scenario realism, model applicability, engineering reliability of algorithms, and technology coordination, identifying major bottlenecks in wideband modeling accuracy, mechanistic interpretability, laboratory–field consistency, and unified evaluation frameworks. Finally, future directions are discussed, including self-healing transformers, digital twins, life-cycle management, integrated mechanism–data modeling, and green low-carbon design. This paper provides a systematic framework to support high-performance design and intelligent operation and maintenance of power transformers in new power systems.

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Under high renewable energy penetration, increasing power-electronic interfacing, and the accelerated deployment of new-type power systems, conventional synchronous-machine-dominated AC grids are facing growing limitations in long-distance bulk power transmission, flexible power regulation, and system resilience, which has promoted VSC-based HVDC transmission and DC grids as major research frontiers. In recent years, substantial progress has been achieved in key enabling technologies, including converter devices and topologies, system control and multi-terminal coordination, DC fault detection and protection, and AC/DC coupled stability mechanisms. In particular, the engineering maturity of modular multilevel converters has established a solid foundation for high-voltage, high-capacity, and multi-terminal DC systems, enabling the transition from point-to-point HVDC links to networked DC grids. However, pronounced trade-offs persist among different technical routes, such as control accuracy versus reliability, power losses versus investment cost, protection speed versus selectivity, and overall system complexity. The coordinated design of converter topologies, system-level control strategies, and DC protection schemes has therefore become a key bottleneck for large-scale and interconnected DC grid deployment. Moreover, several fundamental challenges remain, including fast and reliable DC fault isolation, stable operation under weak-grid and passive-network conditions, and planning and economic evaluation methodologies for DC grids. From a system perspective, this paper reviews the core enabling technologies of VSC-based DC grids, summarizes recent advances in devices and topologies, control and coordination, protection and stability, and representative applications, and comparatively analyzes the applicability and inherent limitations of different technical schemes. Finally, current research bottlenecks and future development trends are identified to support engineering practice, standard development, and frontier research on DC grids.

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Against the rapid growth of the energy internet, electric transportation, and intelligent equipment, magnetically coupled wireless power transfer (MC-WPT) has become a key solution for medium- and short-range high-power wireless energy delivery, owing to its non-radiative transmission, intrinsic electromagnetic safety, and strong environmental adaptability. This paper presents a mechanism-oriented and systematic review of the theoretical foundations and technological evolution of MC-WPT, focusing on magnetic coupling structure optimization and mutual inductance modeling, compensation topologies and their parameter sensitivity, coordinated design of power electronics and control, dynamic wireless power transfer mechanisms, and electromagnetic compatibility and safety constraints. Representative solutions are comparatively analyzed in terms of performance limits, applicable scenarios, and inherent trade-offs, revealing the coupled and synergistic roles of structure, circuit parameters, and control strategies. Key challenges for large-scale and standardized deployment—including interoperability, operational stability under complex conditions, and incomplete safety and standardization frameworks—are further identified, and future trends toward advanced magnetic materials, intelligent control, and deep integration with power grids and transportation systems are discussed. This review provides a unified conceptual framework to support theoretical research, engineering design, and standard development for MC-WPT technologies.

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Against the global shift toward low-carbon, high-efficiency energy systems, exploiting remaining oil in middle- to late-stage reservoirs is increasingly challenging, highlighting the need for green, intelligent, and reservoir-adaptable enhanced oil recovery (EOR) technologies. Among emerging solutions, nanomaterial flooding, microbial EOR (MEOR), and smart waterflooding attract attention for their environmental friendliness, controllable costs, and synergistic mechanisms. Nanomaterials enhance recovery via wettability alteration, interfacial tension regulation, mobility control, and pore-structure modification; MEOR leverages microbial metabolites and biochemical reactions for in-situ reservoir regulation; smart waterflooding improves displacement through engineered multi-ion effects on interfacial chemistry, mineral reactions, and clay ion exchange. Despite progress in mechanisms, simulations, and field trials, challenges remain in extreme-reservoir adaptability, unified modeling, environmental assessment, and large-scale cost. Future work should focus on multiscale mechanism coupling, synergistic material–microbial optimization, dynamic smart-water control, and AI-driven parameter inversion for sustainable, intelligent EOR. This review summarizes scientific foundations, advances, and application trends, and outlines collaborative development pathways and key scientific challenges for efficient, green reservoir exploitation.

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Full Waveform Inversion (FWI), as a key technology for high-resolution seismic imaging and parameter estimation, has undergone rapid development in oil and gas exploration in recent years. Driven by advances in multi-scale acquisition systems, high-performance numerical simulation, and the expansion of intelligent algorithms, FWI has achieved continuous breakthroughs in multi-component, multi-mode, and multi-scale joint inversion, enabling an effective transition from theoretical refinement to engineering practice. Meanwhile, emerging approaches such as deep-learning-assisted inversion, the integration of physical priors, data assimilation, and uncertainty quantification have significantly enhanced the stability and efficiency of inversion workflows, fostering the formation of a “data-driven–physics-driven” hybrid paradigm. However, FWI still faces key bottlenecks—including strong nonlinearity with multiple minima, sensitivity to the initial model, noise amplification, and high computational costs—which constrain its broader applicability. Looking ahead, the intelligent evolution of FWI is advancing toward coupled frameworks that incorporate digital-twin simulation, generative models, and quantum computing, aiming to build high-precision inversion systems with adaptive optimization and multi-source data integration. This review systematically synthesizes the theoretical evolution, technical challenges, and intelligent development pathways of FWI, providing a comprehensive reference and guidance for high-fidelity imaging and intelligent inversion in oil and gas exploration.

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Driven by the dual imperatives of global energy transition and high-quality development of the oil and gas industry, smart oilfields are rapidly evolving from digitalization toward autonomy, characterized by enhanced full-domain sensing, deepened data intelligence, strengthened cyber–physical integration, and increasingly autonomous closed-loop decision-making. Recent advances in the Internet of Things and sensing technologies have significantly expanded real-time data acquisition across oilfield production systems, while big data, cloud computing, and cloud–edge collaboration enable efficient governance and integration of multi-source heterogeneous data. Artificial intelligence and machine learning have demonstrated clear advantages over traditional approaches in key workflows such as reservoir characterization, production optimization, and predictive equipment diagnostics. Digital twin technologies further support multi-scale coupling and dynamic evolution of reservoir–wellbore–surface systems, providing a cyber–physical basis for autonomous decision-making. Despite differences in real-time performance, interpretability, scalability, and application scenarios, these technologies exhibit strong integration potential, including the synergy between mechanistic and data-driven models, the complementarity of edge intelligence and cloud computing, and the coupling of AI models with digital twins. However, challenges remain in data quality and security, model generalizability, system integration and standardization, and the alignment of organizational structures and workforce capabilities. Looking ahead, emerging technologies such as edge intelligence, reinforcement learning, federated learning, generative AI, and industry-specific foundation models are expected to enable cross-scale perception, cross-system collaboration, and autonomous decision-making. This article reviews the technological evolution from digitalized to autonomous smart oilfields, evaluates the strengths and limitations of core technology systems, and outlines future research directions for next-generation autonomous oilfields.

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The ultra-low-permeability sandy conglomerate reservoirs of the Baikouquan Formation in the Junggar Basin are characterized by pronounced heterogeneity and complex pore–throat architectures, highlighting the need to clarify the dominant controls on fluid seepage to improve the accuracy of reservoir flow-capacity evaluation. Given that conventional pore–throat parameters often fail to capture the true migration behavior of reservoir fluids, this study aims to identify the key pore–throat scale governing seepage and to elucidate its underlying physical mechanisms. Using representative core samples, we integrated casting thin sections, conventional mercury intrusion, and constant-rate mercury intrusion to achieve high-resolution characterization of pore–throat structures and to delineate effective flow channels. The results show that the sandy conglomerates of the Baikouquan Formation exhibit poor pore–throat sorting and limited connectivity, and that the correlation between average pore–throat size and seepage capacity is weak; actual fluid flow is predominantly controlled by mainstream throats with favorable connectivity and appropriate radii, whose geometric attributes dictate the dominant pathways for gas–liquid transport. Further analysis reveals that the radius of mainstream throats more accurately captures true seepage behavior than traditional metrics such as mean or peak throat radius, thereby serving as a key indicator for evaluating flow capacity in ultra-low-permeability sandy conglomerate reservoirs. This study deepens the understanding of seepage mechanisms in complex conglomeratic reservoirs and provides quantitative support for horizontal-well placement, volumetric stimulation design, and enhanced oil recovery.

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The hydrocarbon generation and expulsion efficiency of lacustrine source rocks is a critical scientific issue for petroleum resource assessment and exploration planning, yet its controlling factors are highly diverse and exhibit strong regional variability, resulting in an incomplete understanding of expulsion mechanisms and limiting the refined prediction of both conventional and unconventional hydrocarbon resources. This study aims to elucidate the generation–expulsion patterns of lacustrine source rocks under varying geological conditions, identify their primary geological controls, and construct typological models applicable to different organic matter types. Based on multiple representative lacustrine petroliferous basins, we systematically collected large-scale source rock samples spanning a wide range of thermal maturity stages, and applied integrated organic geochemical analyses, pyrolysis-based hydrocarbon generation potential testing, and geological thermal evolution modeling to extract key indicators of expulsion efficiency, followed by multivariate statistical analysis and model development for comprehensive assessment. The results reveal that lacustrine source rocks exhibit broadly consistent generation–expulsion behaviors across basins, with thermal maturity serving as the dominant factor governing expulsion efficiency, while different organic matter types display distinct expulsion patterns, and the effects of basin structure, depositional environment, and fault activity are comparatively minor. On this basis, we establish a universal typological model of hydrocarbon generation and expulsion efficiency, enabling quantitative characterization of expulsion behaviors across organic matter types. The findings not only enhance the understanding of geological controls on lacustrine source rock generation–expulsion efficiency but also provide robust model tools and theoretical support for the assessment of conventional and unconventional hydrocarbon resources.

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In the context of global energy transition and the rapid development of unconventional hydrocarbons, elucidating the structural controls and dynamic mechanisms governing tight-oil accumulation systems is crucial for improving reserve prediction accuracy and optimizing exploration strategies. Focusing on persistent gaps in understanding the structural framework, reservoir-space evolution, and fluid-migration processes, this study investigates the Yanchang Formation of the Ordos Basin using an integrated workflow that combines detailed core observations, fluid-geochemical analyses, reservoir microstructural characterization, diagenetic and petroleum-system dynamic simulations, and three-dimensional geological modeling. The results demonstrate that tight oil in the Yanchang Formation exhibits pronounced stratigraphic zonation and composite structural–sedimentary control, with fluid compositions showing progressive light-hydrocarbon enrichment and phase differentiation. The reservoir’s effective storage capacity is jointly governed by multiscale pore systems, diagenetic sequences, and fracture networks, while short-distance migration driven by hydrocarbon-generation overpressure represents the dominant accumulation mechanism. On this basis, a comprehensive accumulation model integrating “structural framework–reservoir space–fluid characteristics–dynamic processes” is proposed, providing a new theoretical foundation and methodological guidance for tight-oil assessment and efficient development in the Ordos Basin and analogous sedimentary basins.

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Under the multiple challenges of global food security, climate change, and sustainable agricultural development, biotechnology-driven intelligent agriculture is emerging as a core direction for future agricultural innovation. Among these advancements, the deep integration of artificial intelligence (AI) and gene editing technologies provides a novel scientific pathway for precise breeding of high-yield, high-quality, and pest- and disease-resistant crops. This paper systematically reviews the application progress and synergistic effects of AI and gene editing in modern crop breeding. AI, through big data analytics, machine learning, and phenotypic prediction models, has significantly improved the efficiency of target selection and genetic improvement, while gene editing technologies—especially the CRISPR-Cas system—have accelerated the discovery and targeted modification of beneficial trait genes owing to their efficiency, specificity, and controllability. The synergistic application of these technologies has established an intelligent breeding framework that enables precise regulation and optimization of complex agronomic traits from the gene to the population level. Representative cases are analyzed to illustrate the crucial roles of AI and gene editing in enhancing agricultural productivity, improving crop quality, and strengthening stress resistance. Furthermore, this study discusses the current challenges in algorithm reliability, editing precision, ethical governance, and policy regulation, and proposes future research directions. Overall, the integration of AI and gene editing is expected to continuously drive agriculture toward greater intelligence, sustainability, and efficiency, providing important technological insights for ensuring global food security and advancing ecological agriculture.

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Soil salinization poses an escalating threat to agricultural productivity and ecosystem stability, underscoring the urgent need for green, efficient, and sustainable remediation strategies. Engineered microbial consortia, characterized by functional synergy, controllable structure, and strong ecological adaptability, have emerged as a cutting-edge approach for combating saline–alkali land degradation. Based on a systematic synthesis of recent advances, this review summarizes the mechanisms through which engineered consortia regulate soil physicochemical properties, reconfigure key biogeochemical cycles, and enhance plant tolerance to salinity and alkalinity. It further discusses design strategies ranging from bottom-up synthetic biology construction to top-down natural community simplification, as well as the application of multi-omics analysis, metabolic flux modeling, and artificial-intelligence-assisted optimization for performance enhancement and system-level stability control. Key scientific issues associated with field deployment—including ecological effects, synergistic benefits, and habitat safety—are examined, alongside major technical bottlenecks such as colonization persistence, functional predictability, environmental compatibility, and scalable manufacturing. Finally, future research directions are proposed across four dimensions: theoretical advancement, intelligent design, engineering system integration, and ecological safety governance. This work aims to provide a critical and integrative framework to guide scientific breakthroughs and promote the sustainable application of engineered microbial consortia in saline–alkali soil remediation.

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Against increasing global pressures on food security and ecological sustainability, soil nutrient management is shifting from experience-based fertilization toward digitalized, intelligent, and precision approaches. This study systematically reviews the scientific evolution, technological framework, and emerging frontiers of precision soil nutrient management. Based on bibliometric analysis, technological comparison, and cross-disciplinary synthesis, a three-tier technical framework is proposed, including multi-source “sky–air–ground” sensing systems, big-data- and AI-driven decision models, and precision implementation technologies such as variable-rate fertilization and integrated water–fertilizer management. Existing studies demonstrate that precision nutrient management can substantially improve fertilizer use efficiency, reduce nitrogen and phosphorus losses, and optimize input–output ratios. However, challenges remain in data standardization, cross-regional model applicability, farmer-level adoption, and equipment costs. Future research should prioritize high-resolution real-time sensing, digital-twin-based process simulation, multi-objective optimization, and cross-scale model integration. Interdisciplinary collaboration and industry coordination will be essential for advancing intelligent and sustainable soil nutrient management.

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Facing multiple pressures such as continuous global population growth, limited arable land resources, and intensified climate change, agricultural production is undergoing a critical stage of transformation and upgrading. As the core driving force of next-generation information technology, artificial intelligence (AI) is profoundly reshaping crop genetic improvement and efficient production systems. This paper systematically reviews the major application progress of AI technology in crop variety improvement and yield enhancement, including key areas such as genomic selection, high-throughput phenotyping, gene editing–assisted breeding, precision agricultural management, intelligent pest and disease identification and control, and crop yield prediction. By comprehensively analyzing the mechanisms through which AI enhances breeding efficiency, optimizes field management, and maximizes resource utilization, this study reveals the scientific value and economic potential of AI in accelerating genetic improvement and promoting sustainable agricultural development. Furthermore, it discusses the challenges faced by AI applications in agriculture, including data heterogeneity, algorithm interpretability, cross-scale model integration, and ethical governance, and proposes future directions and research priorities. The paper aims to provide a systematic reference for agricultural technological innovation, promote the deep integration of AI with modern agriculture, and advance global food security and green agricultural development.

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Global climate warming has increased the exposure of agricultural ecosystems to extreme heat events, severely constraining crop yield formation and the stability of field production. As a key component of green agricultural technologies, novel biostimulants and exogenous protectants have emerged as research hotspots due to their capacity to enhance crop stress tolerance, promote metabolic homeostasis, and strengthen environmental adaptability. This review systematically summarizes the physicochemical characteristics, crop response patterns, and application progress of various emerging biostimulants—including nanomaterials, plant-derived signaling molecules, natural active extracts, and functional microorganisms—and highlights their roles in core physiological pathways such as hormone-mediated coordinated regulation, maintenance of reactive oxygen species homeostasis, osmotic adjustment and metabolic reprogramming, induction of heat-shock proteins and chaperone systems, and protection of photosynthetic structures and energy conversion processes. On this basis, key bottlenecks in current research are further identified and analyzed, including the complexity of biostimulant compositions and action targets, insufficient systematic understanding of mechanisms across cellular and population levels, and the incomplete evaluation of safety and field adaptability. The review also outlines future research directions, such as the integrated application of multi-omics and spatiotemporal dynamic monitoring technologies for mechanism elucidation, the design of smart delivery and precision slow-release systems, and the construction of coordinated “microbe–biostimulant–crop” response frameworks. Overall, this work aims to provide theoretical support and methodological references for the development of novel biostimulants, the regulation of crop thermotolerance, and the advancement of sustainable agricultural systems under increasing heat stress.

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Climate change has imposed unprecedented pressure on global agricultural ecosystems, with the increasing frequency of extreme weather events, rising temperatures, and water scarcity posing serious threats to crop production stability and food security. To address these challenges, breeding climate change-adaptive crops has become a critical direction for achieving sustainable agricultural development. This paper systematically elaborates on the concept and breeding objectives of climate-adaptive crops and analyzes their adaptive mechanisms under various climate stress conditions. It further summarizes major breeding strategies, including traditional hybrid breeding, marker-assisted selection, genome editing, epigenetic regulation, and artificial intelligence-driven smart breeding technologies. By integrating typical case studies on drought tolerance, heat resistance, salinity and alkalinity tolerance, and pest and disease resistance, the paper demonstrates the pivotal role of multidisciplinary integration in enhancing crop environmental adaptability. Finally, it discusses the technological bottlenecks, genetic resource limitations, and policy and ethical challenges in this field, emphasizing the importance of international cooperation and open data sharing. The study aims to provide scientific insights and strategic guidance for building climate-resilient agricultural systems and promoting global food security.

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The continuous expansion of saline–alkali land has posed a severe threat to global food security, ecological stability, and sustainable agricultural development. Although conventional physical, chemical, and biological remediation technologies have achieved notable progress, they remain constrained by high costs, limited stability, and uncertain long-term effectiveness. As emerging soil-regulating materials, nanomaterials exhibit unique advantages—such as high specific surface area, active interfacial reactivity, and tailorable functionality—in improving soil structure, regulating salt-ion migration, enhancing water and nutrient retention, promoting directional nutrient release, and modulating microbial community activity. This review systematically summarizes the major mechanisms by which nanomaterials contribute to saline–alkali soil improvement, including structural optimization of soil physicochemical properties, precise regulation of ion fluxes and water behavior, and their emerging roles in synergistic water–fertilizer–pesticide management and intelligent soil environmental monitoring. It further evaluates the environmental behavior and potential ecological risks of nanomaterials, as well as the engineering, economic, and regulatory challenges associated with large-scale application. In light of current research gaps, future development should focus on constructing multifunctional and biodegradable nanocomposite systems, quantitatively elucidating nanomaterial–microbe interactions, and establishing integrated technological platforms for “intelligent sensing–precision regulation–sustainable remediation” tailored to saline–alkali land. Through critical synthesis and frontier analysis, this review clarifies the scientific basis and technological potential of nanomaterials for sustainable saline–alkali land management, providing theoretical guidance and strategic directions for developing safe, efficient, and sustainable agricultural soil remediation systems.

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With the global shift of agriculture toward efficiency, sustainability, and eco-friendliness, precision fertilization and dynamic soil nutrient monitoring technologies are playing an increasingly vital role in improving crop yields and nutrient use efficiency while reducing environmental impacts. This paper systematically reviews the current applications and recent advances of big data technologies in precision agriculture, with a focus on the development of big data–based precision fertilization techniques and dynamic soil nutrient monitoring systems. First, the principles and limitations of traditional soil nutrient monitoring methods are reviewed, highlighting the advantages of data-driven monitoring approaches in terms of real-time performance, spatial resolution, and decision support. Then, the critical roles of sensor networks, remote sensing data integration, machine learning, and optimization models in soil nutrient monitoring and fertilization decision-making are discussed in depth, accompanied by case studies that demonstrate the practical outcomes and technical bottlenecks of precision fertilization systems in real-world agricultural production. Furthermore, the core challenges in integrating big data with precision fertilization—such as insufficient data standardization, limited model generalization, and inadequate economic feasibility and policy support—are summarized. Finally, future research directions and development suggestions are proposed from the perspectives of multi-source data fusion, intelligent decision algorithm optimization, and cross-scale system integration. This paper aims to provide theoretical support and practical guidance for promoting the innovative development of precision agriculture technologies and advancing sustainable agricultural production.

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Climate change has profoundly affected the global water cycle and the balance of supply and demand for agricultural water resources, thereby threatening food security and sustainable agricultural development. This paper systematically reviews the dual impacts of climate change drivers—including rising temperatures, changes in the spatiotemporal patterns of precipitation, and the increasing frequency of extreme climate events—on the supply and demand of agricultural water resources, with particular attention to their mechanisms affecting surface water, groundwater recharge, and irrigation demand. The findings indicate that water scarcity in arid and semi-arid regions is becoming increasingly severe, the uneven spatiotemporal distribution of water in temperate and tropical regions is intensifying, and river basins dependent on glacier and snowmelt are facing significant risks. In response to these challenges, this study evaluates the potential of various adaptation measures, including efficient water-saving irrigation technologies (such as drip irrigation, micro-sprinkling, and intelligent regulation systems), enhancement of crop drought tolerance and resilience (through conventional breeding and emerging techniques such as gene editing), as well as refined water resource management and policy optimization (encompassing agricultural water pricing regulation and allocation mechanisms). Moreover, the application prospects of climate model simulations and uncertainty forecasting in water resource planning are discussed, while highlighting the limitations of current research in multi-scale integrated assessment, interdisciplinary synthesis, and policy translation. Finally, future research directions are proposed. The purpose of this paper is to provide scientific references for policymakers, agricultural managers, and the research community to enhance the adaptability and resilience of agricultural water resource systems and ensure the long-term sustainability of agriculture.

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With the deep integration of big data and artificial intelligence (AI) technologies in agricultural biology, molecular regulation research on crop starch synthesis has entered a new era of data-driven intelligence. This paper provides a systematic review of recent advances in big data- and AI-assisted gene mining for potato starch synthesis, focusing on the integration strategies of multi-omics data—including genomics, transcriptomics, and metabolomics—and their roles in identifying key functional genes. By introducing machine learning and deep learning models, it enables the efficient screening of starch synthesis-related genes and regulatory networks from large-scale omics datasets, offering new technological pathways to elucidate the genetic basis of starch biosynthesis in potatoes. Furthermore, the study explores data-driven molecular design and precision breeding models, highlighting the potential of AI algorithms in optimizing starch content and quality. Based on representative case studies, it summarizes major achievements and existing technical challenges in the field, while outlining future directions in algorithm optimization, data standardization, and cross-scale breeding system construction. Overall, this review aims to provide a systematic overview and methodological framework for potato starch synthesis research empowered by big data and AI, serving as a scientific reference and technical foundation for subsequent functional gene discovery and precision breeding practices.

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Wheat, as one of the most important staple crops worldwide, faces severe challenges of heat stress on yield and quality under global warming, highlighting the urgent need for systematic identification of heat-tolerant germplasm and dissection of heat tolerance genes to ensure food security and advance molecular breeding. In this study, a rapid seedling-stage heat tolerance evaluation system was established using artificial climate chamber treatments with diverse wheat germplasm resources, and heat tolerance was systematically assessed based on seedling survival rate. On this basis, a genome-wide association study (GWAS) was conducted using single-nucleotide polymorphism arrays, candidate genes were screened by integrating transcriptome data, and the expression patterns of several genes were validated by qPCR. The results revealed significant variation in heat tolerance among accessions, with some materials exhibiting strong heat tolerance potential; multiple significant loci associated with heat tolerance were identified by GWAS, and a set of potential candidate genes was discovered; further validation revealed several genes playing key roles in wheat heat tolerance. This study not only established an efficient evaluation system for seedling-stage heat tolerance in wheat but also clarified its genetic basis and identified key candidate genes, providing important theoretical insights and practical support for heat-tolerant wheat germplasm innovation and molecular design breeding.

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Under increasing water resource constraints and the transition of rice production toward water-saving and high-efficiency systems, alternate wetting and drying (AWD) irrigation has become an important cultivation strategy. However, the physiological mechanisms underlying its interaction with plant growth regulators in regulating rice growth and yield remain unclear. This study investigated the combined effects of AWD irrigation and exogenous plant growth regulators on photosynthetic function, endogenous hormone balance, assimilate translocation, and yield formation in rice. Field experiments were conducted under different AWD regimes with plant growth regulator treatments, and post-heading leaf photosynthetic traits, endogenous hormone contents, dry matter accumulation and partitioning, and yield components were systematically analyzed. The results showed that moderate AWD irrigation delayed leaf senescence, optimized endogenous hormone balance, and enhanced assimilate accumulation and translocation to panicles, thereby promoting yield formation. The application of plant growth regulators further improved leaf photosynthetic capacity and assimilate allocation efficiency, showing a clear synergistic effect with AWD irrigation. This interaction enhanced rice yield stability by prolonging the functional duration of photosynthetic leaves and improving assimilate transport efficiency. Overall, the coordinated application of AWD irrigation and plant growth regulators provides an effective physiological regulation strategy for improving rice productivity under water-saving cultivation conditions.

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As an important type of high-quality specialty wheat, strong gluten wheat plays a critical role in ensuring food security and supporting the sustainable development of the flour processing industry, with nitrogen (N) management being a key factor for achieving high yield, superior quality, and efficient N utilization. Current studies largely focus on the effects of a single N application rate, while systematic investigations on the synergistic effects of N application rate, base-topdressing ratio, and N forms remain limited. In this study, field experiments were conducted in Hebei Province, China, to comprehensively evaluate the impacts of different N management strategies on yield components, processing quality, and N use efficiency of strong gluten wheat. The results showed that N application rate significantly affected yield and quality: with increasing N input, spike number increased, thousand-grain weight decreased, while grain crude protein and wet gluten contents were markedly enhanced. An appropriate base–topdressing ratio effectively balanced yield, quality, and N use efficiency. Distinct roles of N forms were observed, with nitrate-N favoring yield improvement and N use efficiency, whereas ammonium-N was more conducive to enhancing processing quality. Overall, the integrated optimization of N application rate, base–topdressing ratio, and N forms achieved synergistic improvements in yield, quality, and N use efficiency. This study not only highlights the significant interactions among N management factors but also proposes a practical model for efficient N utilization, offering both theoretical insights and practical guidance for the sustainable production of high-quality specialty wheat.

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Water-saving irrigation, while improving water use efficiency in paddy fields, poses new challenges to the migration and transformation of soil phosphorus, whereas the mechanism of biochar—a key soil amendment—in phosphorus cycling of paddy soils remains unclear. In this study, a water-saving irrigated paddy field was used as the research object, and different biochar application rates were set to systematically evaluate the responses of available phosphorus (AP), total phosphorus (TP), phosphorus activation coefficient (PAC), and adsorption–desorption characteristics through field experiments and laboratory analyses, with Langmuir and Freundlich isotherm models applied for fitting. The results showed that an appropriate biochar application promoted the increase of AP and PAC in surface soils while reducing TP content, thereby significantly improving phosphorus activation. Meanwhile, biochar altered the interfacial reaction characteristics of soil phosphorus, manifested as a dynamic regulatory effect on adsorption capacity and desorption potential, with more pronounced trends observed under higher application rates. The findings suggest that biochar optimises the pathways of phosphorus migration and transformation and regulates interfacial reactions in paddy soils under water-saving irrigation, thus enhancing phosphorus bioavailability and utilisation efficiency. This study provides a scientific basis and technical support for the efficient use and sustainable management of phosphorus resources in water-saving irrigated paddy fields.

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Basic helix–loop–helix (bHLH) transcription factors are widely present in plants and play a central role in adaptation to abiotic stresses. However, functional studies of bHLH family members in potato remain limited, which restricts their application in stress-resilient breeding. In this study, the potato StFBH3 gene was selected as the research focus to elucidate its role and molecular regulatory mechanism in stress response. StFBH3 overexpression lines were generated through gene cloning and vector construction, and their expression and functional characteristics were analyzed under osmotic stress, high salinity, and exogenous abscisic acid treatments. Gene expression patterns were assessed by qPCR, while physiological and biochemical traits, including chlorophyll content, root length, relative water content, and superoxide dismutase (SOD) activity, were measured. The results showed that StFBH3 was highly expressed in roots and leaves and significantly induced under multiple stress conditions. Overexpression lines exhibited enhanced tolerance to osmotic and salt stress, as evidenced by higher relative water content, chlorophyll content, and SOD activity compared with controls. At the molecular level, StFBH3 likely exerts positive regulatory effects by modulating downstream genes such as KAT1. These findings demonstrate that StFBH3 plays a positive regulatory role in potato adaptation to abiotic stress and provide new theoretical insights and potential molecular targets for elucidating the mechanisms of bHLH transcription factors and improving stress resistance through genetic breeding.

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