With the rapid development of big data technology, its application in reservoir numerical simulation and drilling optimization is gradually becoming a key method to improve oilfield development's efficiency and scientific nature. This paper systematically reviews the latest advancements in big data technology in reservoir modeling and optimization, focusing on using massive geological, seismic, and production data to construct high-precision 3D reservoir models and improve drilling plan design and implementation through numerical simulation and optimization techniques. First, the core role of big data in reservoir modeling is analyzed and compared with traditional methods, highlighting its unique advantages in data integration, analysis, and prediction. Next, the process of big data-based reservoir modeling is elaborated, including data collection and preprocessing, model construction and calibration, and the application of optimization methods. Subsequently, the specific applications of numerical simulation in drilling optimization are discussed, especially the technological pathways that use real-time monitoring data and intelligent decision support systems to improve drilling success rates and reduce development costs. Typical case studies demonstrate the effectiveness of big data-driven reservoir numerical simulation and drilling optimization technologies in actual oilfield development, and future research directions and potential challenges are also proposed. This paper aims to provide scientific technical guidance and theoretical references for oilfield development through systematic review and analysis, promoting the intelligent upgrading of reservoir management and development technologies.

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With the deepening development of oilfields towards complex reservoirs and heterogeneous fluid behavior, traditional reservoir, and fluid dynamic simulation methods face significant limitations regarding accuracy and efficiency. In recent years, the rapid development of big data technology has provided new technical pathways and innovative opportunities for oilfield development. This paper systematically explores the application potential of big data technology in reservoir and fluid dynamic simulation, with a focus on how big data analysis can achieve high-precision prediction of oil and gas flow behavior and intelligent optimization of injection and production schemes. First, the theoretical framework of reservoir and fluid dynamic simulation is introduced, highlighting the limitations of traditional methods in modeling complex reservoir structures and fluid properties. Next, applying big data technology, combined with machine learning and deep learning algorithms, is detailed, emphasizing the accurate modeling of reservoir characteristics and fluid flow patterns through the mining and analysis of massive multi-source data. Based on this, big data-driven optimization strategies for water injection and oil production are proposed, along with the effectiveness and insights from practical oilfield applications. Finally, the paper summarizes the challenges of big data technology in oilfield development, forecasts the future direction of intelligent oilfields, and emphasizes the importance of interdisciplinary integration in promoting injection-production optimization and intelligent decision-making. This paper aims to provide theoretical support and practical guidance for oilfield development, helping to improve development efficiency and economic benefits.

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Deep-sea carbonate reservoirs hold significant strategic importance in global oil and gas resources due to their unique depositional environments, complex diagenetic processes, and high heterogeneity, which pose major challenges for exploration and development. This paper systematically reviews the genesis mechanisms and classification characteristics of deep-sea carbonate reservoirs, focusing on the differences in depositional patterns and diagenetic processes compared to terrestrial carbonate reservoirs. By integrating recent advances in understanding pore structure, permeability, and the controlling factors of reservoir heterogeneity, the geological characteristics of deep-sea carbonate reservoirs and their influence on hydrocarbon accumulation are thoroughly analyzed. The paper further examines recent developments in seismic exploration, numerical simulation, and multi-physics data integration technologies, outlining the current status of exploration and development while identifying key technical bottlenecks under complex geological conditions. Future directions are proposed based on current research trends, including refining reservoir prediction models, developing novel exploration technologies, and incorporating ecological and environmental considerations. This paper aims to provide a comprehensive theoretical and technical reference for the academic community, supporting the efficient and sustainable development of deep-sea carbonate reservoirs.

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Carbonate reservoirs exhibit highly heterogeneous pore systems, complex fracture networks, and unique hydrodynamic characteristics, making hydrocarbon migration and accumulation mechanisms difficult to predict accurately. Traditional hydrocarbon migration models often struggle to address complex geological conditions, failing to capture migration pathways and reservoir distribution patterns precisely. In recent years, artificial intelligence (AI) technologies, particularly machine learning and deep learning models, have emerged as innovative solutions due to their strengths in big data analysis and complex system modeling. By integrating historical and real-time monitoring data, AI can construct data-driven hydrocarbon migration prediction models, effectively simulating hydrocarbon distribution and retention mechanisms under varying geological conditions. Additionally, AI dynamically optimizes hydrodynamic parameters, improving the efficiency and accuracy of development strategies. This paper systematically evaluates the current applications of AI in studying carbonate reservoir formation mechanisms and hydrocarbon migration simulation, with a focus on integrating AI models with hydrodynamic simulations to optimize migration behavior. Finally, AI-driven intelligent development strategies are proposed to support the efficient development and refined management of carbonate reservoirs.

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Carbonate reservoirs are a crucial component of global oil and gas resources, characterized by complex pore structures, significant heterogeneity, and multi-stage diagenesis, all of which profoundly impact reservoir storage performance and development efficiency. In recent years, rapid advancements in geophysical imaging, reservoir characterization, and data analysis technologies have led to significant theoretical and practical progress in carbonate reservoir research. This paper systematically reviews the genetic characteristics, depositional environments, and diverse pore types of carbonate reservoirs and explores the reshaping mechanisms of diagenesis on reservoir storage performance. Furthermore, it highlights recent advances in the quantitative characterization of reservoirs using modern techniques such as seismic inversion, nuclear magnetic resonance, and computed tomography. It also summarizes breakthroughs in reserve estimation and uncertainty analysis. Finally, key challenges in carbonate reservoir evaluation are discussed, including technical bottlenecks in deep reservoir assessment and difficulties in fine-scale heterogeneity characterization. Future research directions are proposed, focusing on high-precision reservoir modeling, integrated applications of emerging technologies, and sustainable development strategies. This study aims to provide insights for scientific research and engineering practices in carbonate reservoir evaluation.

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As oilfield development enters a more complex and refined stage, traditional static water injection strategies struggle to meet the demands of precise reservoir management under dynamic conditions. The emergence of big data technology provides new theoretical and practical support for optimizing oilfield water injection strategies. This paper systematically explores the application of big data in dynamic water injection management, clarifying the core concepts and advantages of dynamic injection strategies. By integrating real-time monitoring, massive data acquisition, and machine learning algorithms, oilfield development can achieve intelligent adjustment and precise optimization of water injection plans based on reservoir dynamics. This study focuses on the big data-driven optimization process for water injection, encompassing model construction, parameter optimization, and decision support. It examines its significant impact on improving injection efficiency and economic benefits. Additionally, through case studies, this paper verifies the practical effectiveness of big data technology in water injection management and demonstrates its feasibility and value through economic analysis. Finally, the paper summarizes key challenges in current technological development, such as data quality, model complexity, and interdisciplinary integration, and envisions the broad prospects of artificial intelligence and big data in driving intelligent and sustainable oilfield development. This paper aims to provide theoretical guidance and technical support for optimizing oilfield water injection strategies based on big data, fostering the intelligent evolution of oilfield development.

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With the increasing complexity of oilfield development and the urgent need to enhance resource extraction efficiency, the application of big data technology in oilfield development and management has gained growing attention. This paper focuses on the big data-driven optimization of combined water flooding and hydraulic fracturing, aiming to explore their synergistic effects and propose data-based optimization strategies to improve recovery efficiency. First, the fundamental principles of water flooding and hydraulic fracturing and their critical roles in enhancing recovery are outlined, highlighting the core value of big data in reservoir characterization, dynamic parameter optimization, and real-time monitoring. Subsequently, an optimization model driven by big data is developed, enabling real-time data acquisition and feedback to dynamically adjust water flooding and fracturing parameters, thereby achieving efficient synergy between the two technologies and significantly improving production efficiency and oilfield development outcomes. Finally, the study summarizes the technical challenges of big data-driven joint optimization. It discusses future development directions, including algorithm improvements, real-time monitoring system upgrades, and multi-source data integration. This paper provides new technological support and scientific insights for efficient oilfield development, with significant theoretical and practical value.

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Carbonate reservoirs, characterized by their complex pore structures and significant heterogeneity, have long faced numerous challenges in oilfield development. Traditional water injection techniques have limited effectiveness in carbonate reservoirs, making it difficult to achieve efficient and uniform displacement of residual oil. In recent years, with the rapid advancement of artificial intelligence and big data technologies, intelligent water injection technology has provided a revolutionary solution for carbonate reservoir development. By integrating real-time monitoring, machine learning models, and optimization algorithms, this technology dynamically regulates injection rates and volumes to achieve precise control over water injection, thereby maximizing extraction efficiency and enhancing reservoir management effectiveness. This paper systematically explores the core mechanisms and key technologies of intelligent water injection in carbonate reservoirs, including data acquisition and processing, intelligent model construction, parameter optimization, and real-time decision support. Through case studies of practical engineering applications, the advantages of this technology in enhancing water injection efficiency and resource recovery are analyzed. Finally, the development prospects of intelligent water injection technology for carbonate reservoirs are discussed, and potential future research directions are proposed. This paper aims to provide theoretical support for the following argument. It also offers practical guidance for intelligent development under complex reservoir conditions.

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Water injection development is a crucial method for enhancing oil recovery, and numerical simulation technology provides essential scientific tools and theoretical support for optimizing the injection process. This paper systematically reviews the fundamental principles of water injection numerical simulation, focusing on the dynamic impact of different injection strategies on reservoir pressure and saturation fields. The application value of high-resolution reservoir modeling in complex fluid migration simulation is elaborated. Furthermore, key optimization strategies, including injection rate, injection volume, and zonal injection, are explored, with a particular emphasis on methods and challenges for enhancing water flooding efficiency in heterogeneous reservoirs. Integrating the latest advances in artificial intelligence and machine learning, this paper highlights their potential in reservoir parameter prediction, model automation, and rapid dynamic forecasting, emphasizing the practical application of intelligent tools in complex reservoir water injection simulations. For geological structures such as fault zones and fractures, the applicability of relevant numerical simulation methods is assessed, providing insights into optimizing water injection strategies. Through in-depth analysis of simulation results, this paper summarizes practical experiences in optimizing water injection based on reservoir characteristics and envisions the future development of intelligent and efficient water injection strategies. The paper aims to provide theoretical guidance and technical support for water injection development in heterogeneous reservoirs, offering new pathways for reservoir management and enhanced oil recovery.

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With the continuous advancement of oil and gas exploration into deepwater and complex formations, traditional sand control technologies face challenges in ensuring both efficiency and safety. Intelligent sand control technology integrates the Internet of Things, artificial intelligence, and big data analytics to enable real-time monitoring of critical parameters such as downhole sand flow, wellbore pressure, and temperature, allowing dynamic adjustment and optimization of sand control measures. AI-driven intelligent screen design optimizes pore size and structure to enhance sand filtration efficiency while reducing pressure loss. This paper systematically reviews the components, core applications, and current challenges of intelligent sand control technology, with a focus on its potential applications in deepwater wells and complex reservoirs. The findings suggest that intelligent sand control technology provides substantial benefits in enhancing well productivity and operational safety. Finally, future development directions are discussed, providing insights for further innovation and application of this technology.

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The application of nanomaterials in reservoir sand control technology demonstrates significant advantages, offering an innovative approach to addressing complex sand control challenges. This paper explores the mechanisms of nanoparticle filling and nanocoating technologies in sand control, focusing on their roles in enhancing the stability of gravel packs and improving the wear resistance and corrosion protection of screens. Leveraging smart oilfield technologies, this study further examines the integration and advancement of nano-based sand control in data-driven reservoir sand monitoring, intelligent prediction, and optimized decision-making, highlighting its potential benefits in enhancing the precision and efficiency of sand control strategies. By summarizing recent experimental studies and field applications, this paper comprehensively evaluates this technology’s economic benefits and environmental sustainability in oilfield development. Finally, the paper envisions the prospects of deep integration between nano-sand control technology and smart oilfields under multidisciplinary collaborative innovation, providing new insights for achieving efficient and environmentally friendly oilfield development.

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With the increasing scale and complexity of power systems, traditional state monitoring and fault diagnosis methods can no longer meet the efficiency and reliability demands of modern power grids. In recent years, the rapid development of big data and artificial intelligence (AI) technologies has provided new opportunities for intelligent monitoring and diagnosis in power systems. This paper systematically examines the key applications of big data technology in power system state monitoring, covering data acquisition, processing, and analysis methods. It explores big data-based fault prediction models in detail. Meanwhile, it provides an in-depth analysis of AI applications in power system fault diagnosis, highlighting the role of machine learning and deep learning algorithms in enhancing fault identification accuracy and prediction precision. Through practical case studies, this paper showcases the successful applications of big data and AI in power systems, discussing the technical challenges and corresponding solutions in their implementation. Furthermore, it presents future development trends in state monitoring and fault diagnosis, emphasizing potential research hotspots and technological breakthroughs. This paper aims to serve as a comprehensive reference for academic researchers and engineering practitioners, contributing to the continuous advancement of power system intelligence.

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With the continuous growth of global demand for renewable energy, efficiently addressing the challenges of utilizing variable energy sources such as wind and solar power has become a key research focus in the energy sector. This paper systematically reviews the progress in the application of big data and artificial intelligence (AI) in renewable energy generation forecasting and optimal scheduling. In generation forecasting, it provides a detailed analysis of the variability characteristics of wind and solar power, highlights the critical role of big data in data acquisition and processing, and discusses the advantages of machine learning and deep learning algorithms in improving forecasting accuracy. This paper summarizes the latest research advancements in dynamic scheduling strategies, AI-driven real-time optimization systems, and intelligent energy storage management, emphasizing their effectiveness in enhancing energy system flexibility and stability. Specifically, it explores the design and validation of simulation experiment frameworks, covering mathematical modeling, scenario setup, optimization algorithm evaluation, and comparative analysis. Finally, the paper outlines future research directions, proposing potential pathways to improve simulation methodologies and discussing cutting-edge trends in AI-driven smart renewable energy systems. This paper aims to provide comprehensive insights for researchers and practitioners, facilitating the efficient utilization of renewable energy and the intelligent transformation of energy systems.

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With the large-scale deployment of renewable energy and the transformation of energy systems towards decentralization and distribution, distributed energy systems and microgrid management have become crucial components of modern energy networks. The rapid development of big data technologies has provided new perspectives and technical support for optimizing Distributed Energy Resources and microgrid management, particularly showing great potential in enhancing system stability and adaptability. This paper provides a systematic review of the current applications and cutting-edge progress of big data in distributed energy optimization and microgrid management. It begins with an overview of the basic principles and system architecture of distributed energy and microgrids, highlighting current research hotspots and challenges. The paper then delves into big data-based energy resource optimization strategies, including load forecasting, resource scheduling, and distributed control technologies. It also analyzes big data-driven microgrid management technologies, focusing on areas such as real-time data analysis, predictive maintenance, adaptive control, and fault diagnosis. Furthermore, it explores the integration of artificial intelligence technologies, discussing the design of intelligent energy management systems and the prospects of deep learning in optimizing energy distribution. Typical case studies are presented to showcase the successful application of big data technologies in real-world distributed energy systems and microgrids, with an evaluation of their adaptability in various application scenarios. Finally, the paper looks ahead to the development directions of big data and artificial intelligence in future energy systems, analyzing the challenges and potential solutions for promoting current technologies. This paper aims to provide researchers and engineers in distributed energy and microgrids with a systematic theoretical framework and practical guidance to help build more efficient, stable, and intelligent energy systems.

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With the continuous integration of high-proportion renewable energy into power systems, system operation is increasingly challenged by the high volatility, low inertia, and strong uncertainty of these energy sources, resulting in issues related to frequency stability, supply-demand coordination, and operational security. Resilience, as a core indicator for evaluating the ability of power systems to withstand disturbances, recover rapidly, and maintain continuous service, is gradually replacing the traditional steady-state security paradigm and becoming a key concept in constructing next-generation power systems. This paper systematically reviews the theoretical evolution and multi-dimensional assessment frameworks of power system resilience and analyzes the dynamic characteristics of systems under high renewable penetration. Based on this, it explores key pathways and technical support mechanisms for resilience enhancement across five dimensions: power generation, transmission networks, loads, energy storage, and control systems. Particular emphasis is placed on the pivotal roles of emerging technologies—such as intelligent sensing, artificial intelligence, self-healing control, and digital twins—in enhancing system responsiveness and intelligence. Finally, the paper highlights that building a new-type power system with high resilience, fast responsiveness, and advanced intelligence requires coordinated advancement across technology innovation, system architecture redesign, policy mechanism improvement, and regional collaborative development. This paper aims to provide systematic theoretical support and feasible technical pathways for the transformation and upgrading of power systems under conditions of high proportions of renewable energy.

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Climate change significantly impacts the distribution and availability of global water resources, increasing the frequency and intensity of extreme weather events such as droughts and floods. This complex issue poses a severe challenge to traditional water resource prediction methods. In recent years, artificial intelligence (AI) technology has gradually emerged as an essential tool for addressing climate change and water resource management problems due to its outstanding performance in big data processing and complex system modeling. This paper provides a comprehensive review of the latest advancements in AI applications for climate data processing, hydrological model optimization, climate impact prediction, and water resource risk warning. It focuses on the effectiveness and practical cases of AI in predicting extreme events such as droughts and floods. Through a systematic review of existing research, this paper summarizes the advantages of AI in improving prediction accuracy and decision-making efficiency, while also exploring key challenges in the field, including data quality assurance, model uncertainty evaluation, and the need for interdisciplinary collaboration. Finally, the paper outlines future directions for AI in water resource management under climate change, aiming to provide scientific references for academic research and policy-making to support global strategies for managing water resources in the face of climate change.

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Global climate change and water resource shortages pose unprecedented challenges to agricultural production and water resource management. Traditional water resource management methods are struggling to address the complex environmental dynamics and varying water demands of different crops. The rapid development of artificial intelligence (AI) offers new opportunities to establish more efficient and intelligent agricultural water resource management systems. This paper systematically explores AI-based virtual experiments and simulation methods for agrarian water resources, proposing an overall design framework for a virtual farm laboratory. It focuses on analyzing core data processing technologies and the integrated application methods of crop growth models, hydrological models, and climate models. The study further investigates the specific applications of AI technologies, such as machine learning, deep learning, and reinforcement learning, in optimizing irrigation strategies, simulating water resource allocation under multiple climate scenarios, improving water use efficiency, and optimizing crop variety selection. Case studies validate the feasibility and practical effectiveness of virtual experiments, revealing the significant value of AI-based virtual experiments in addressing climate change, optimizing agricultural water efficiency, and enhancing agricultural production management. This paper aims to provide technical insights for research and practice in agricultural water resource management and highlights key future research directions and potential challenges.

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In the context of the agricultural transformation towards precision and intelligence, optimizing nitrogen management in rice fields to improve production efficiency and resource utilization has become an important research topic. This paper proposes and investigates an intelligent nitrogen management solution based on a regional agricultural big data platform. By integrating multi-source data such as soil properties, climatic conditions, fertilization history, and irrigation management, combined with artificial intelligence (AI) technologies, a customized nitrogen management strategy suitable for large-scale rice fields is developed. The research includes: (1) analyzing the system architecture and core technologies of the agricultural big data platform, explaining the methods of data collection, cleaning, fusion, and processing; (2) constructing an AI-based nitrogen demand prediction model and precision fertilization strategies; (3) exploring the synergistic effect of organic fertilizers and irrigation management in improving nitrogen use efficiency and proposing optimization pathways; (4) developing an AI-assisted decision-making system to achieve dynamic optimization of rice field resources. Case analysis verifies that the platform significantly improves nitrogen use efficiency and rice field productivity, demonstrating its potential to enhance regional agricultural resource utilization and achieve sustainable development. This paper provides scientific technological support for nitrogen management in rice fields and offers an important reference for the development of smart agriculture.

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With the rapid development of precision agriculture technology, simulating and optimizing rice paddy production processes using big data and artificial intelligence (AI) has become an important research direction. This study proposes a virtual rice paddy laboratory framework based on big data and AI, integrating multi-source data such as soil, climate, and crop growth to establish a prediction and optimization model for nitrogen use efficiency (NUE). Using the virtual laboratory, the rice paddy production process under different soil types and climate conditions was simulated, and various water and fertilizer management strategies were designed and evaluated for their impact on NUE. Based on advanced machine learning algorithms, a dynamic simulation model for crop growth and nitrogen utilization was developed, and its prediction accuracy was validated, leading to the optimization of water and fertilizer management plans. Simulation results indicate that NUE can be significantly improved under different soil conditions through targeted adjustments in management strategies. This study presents a low-cost and efficient optimization method for paddy field management, providing technical support for the future intelligent development of precision agriculture.

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Gene-environment interaction research is a core field in modern agricultural science, aiming to elucidate the complex relationships between genotypes and environmental factors (such as climate change, soil properties, and agricultural management practices) to provide theoretical guidance and technical support for crop adaptive breeding. This paper first reviews the fundamental theoretical framework of gene-environment interactions and analyzes the variability of gene expression under different environmental conditions and their physiological mechanisms. It then explores the application of big data and advanced technologies—such as high-throughput genomics, phenotypic data acquisition, environmental variable modeling, and machine learning methods—in gene-environment interaction research, particularly how these technologies enhance research efficiency, accuracy, and predictive capability. The paper further discusses the practical applications of gene-environment interactions in agriculture, emphasizing their contributions to improving crop environmental adaptability, stress resistance, and yield potential. The analysis of representative agricultural cases highlights the broad application prospects of gene-environment interactions in crop breeding and their profound impact on agricultural productivity and sustainability. Finally, the paper presents future research trends and challenges in this field, emphasizing the importance of interdisciplinary collaboration, data sharing, and the integration of intelligent technologies as key drivers of innovation. This paper aims to systematically summarize the theoretical and technological advancements in gene-environment interaction research, provide scientific insights for improving crop environmental adaptability, and propose new technological pathways and strategies for promoting sustainable agricultural development.

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Saline-alkali soils are widely distributed in arid and semi-arid regions worldwide, posing significant challenges to crop growth and nutrient uptake. Salt accumulation and alkaline conditions not only inhibit the absorption of essential nutrients but also deteriorate soil structure, ultimately affecting agricultural productivity and food security. This study systematically analyzes the effects of different tillage practices (such as deep tillage and no-tillage) and crop rotation patterns on the distribution of soil micronutrients and crop nutrient uptake efficiency, proposing optimized nutrient management strategies adapted to saline-alkali conditions. Additionally, it explores the effectiveness of precision fertilization techniques, soil amendments (such as organic matter and gypsum), and the potential value and practical applications of big data analytics and smart agriculture technologies in nutrient management. By integrating multidimensional technologies and management approaches, this study proposes a comprehensive framework for crop nutrient management in saline-alkali soil environments, aiming to enhance nutrient use efficiency, improve soil health, and promote sustainable agricultural development.

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Strain screening and optimization have significant applications in agriculture, environmental remediation, and industrial production. However, traditional methods are often constrained by high costs, low efficiency, and insufficient accuracy in complex environments. The rapid advancement of artificial intelligence (AI) offers innovative solutions to these challenges. Through machine learning and deep learning algorithms, AI can efficiently predict the functional traits of strains from vast experimental data, optimize culture conditions, and significantly enhance the efficiency and accuracy of high-throughput screening. This paper systematically reviews the technical framework of AI in strain screening and optimization, discusses its key applications in optimizing culture conditions and high-throughput screening, and explores its potential value in cutting-edge fields such as strain design, synthetic biology, and gene editing. Additionally, this paper analyzes the main limitations of AI technology in strain research, including insufficient data quality, poor model interpretability, and ethical and regulatory challenges. Finally, future research directions are proposed, along with potential solutions, to provide valuable academic references for the AI-driven strain screening and optimization field.

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Climate change and diverse environmental stresses (such as drought, high salinity, and temperature fluctuations) pose significant challenges to the sustainable development of agriculture, severely affecting crop growth, yield stability, and global food security. As a result, improving crop stress resistance and environmental adaptability has become a core topic in modern agricultural research. Artificial intelligence (AI), a cutting-edge technology, has demonstrated immense potential in enhancing crop stress resistance due to its powerful data analysis and pattern recognition capabilities. By integrating genomics, phenomics, and environmental data, AI technology can efficiently screen stress-resistant genes, decipher complex gene networks, and, when combined with gene editing techniques, precisely improve crop traits. Moreover, machine learning and deep learning algorithms play a crucial role in phenotype data processing, multi-trait optimization, and the design of breeding strategies for environmental adaptability, significantly enhancing breeding efficiency and precision. This review systematically summarizes the latest applications of AI in improving crop stress resistance and environmental adaptability, focusing on key technologies such as AI-assisted stress gene identification, gene editing optimization, and phenotypic data mining, along with practical case studies. The article also discusses the main challenges and technical bottlenecks in current research and looks forward to the future development of AI in intelligent breeding. By exploring AI-driven new breeding models, this paper aims to provide theoretical foundations and technical references for agricultural technological innovation and sustainable development.

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Calcium is an essential element for plant growth and development, playing a crucial role in maintaining cell structure stability, signal transduction, and metabolic regulation. As a key secondary messenger, calcium regulates crop responses to various stresses, including drought, salt stress, and diseases. This review systematically summarizes the mechanisms by which calcium enhances crop stress resistance, with a focus on its core functions in cellular signal transduction, particularly the signaling pathways mediated by calcium-dependent protein kinases and calmodulin. The role of calcium under drought stress is specifically analyzed, covering its regulation of stomatal movement, optimization of water use efficiency, and modulation of drought-related gene networks. Furthermore, the review elaborates on the synergistic effects of calcium in maintaining ion homeostasis and enhancing the antioxidant system under salt stress, as well as its role in gene expression regulation. Additionally, the critical functions of calcium in the crop immune system are discussed, particularly the dynamic changes in calcium signaling upon pathogen invasion and its activation of defense-related genes. Based on recent research progress, this review explores the crosstalk between calcium and other signaling molecules and their integrative response mechanisms under complex stress conditions. Finally, the potential applications of calcium signaling pathways in molecular breeding and agricultural production are highlighted. This review aims to provide theoretical support for further elucidating the molecular mechanisms of calcium in crop stress resistance and serves as a reference for agricultural practices involving calcium signal regulation.

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Global climate warming poses a severe challenge to agricultural production, with high temperatures, drought, and climate variability profoundly affecting crop growth cycles and productivity. Gene editing technology, exact tools such as CRISPR-Cas9, offers a revolutionary approach to regulating crop growth cycles and enhancing environmental adaptability. This paper systematically summarizes the development of gene editing technology and its applications in regulating the crop growth cycle, including the modulation of key traits such as flowering time, maturation time, photosynthetic efficiency, and water use efficiency. By precisely targeting key genes, researchers have successfully developed crop varieties with improved heat tolerance, drought resistance, and adaptability to variable climate conditions, laying a foundation for enhancing agricultural resilience and stability. Furthermore, this paper focuses on specific strategies and case studies of gene editing applications in addressing climate change while evaluating their potential impacts and limitations. Despite current challenges such as technical breakthroughs and ethical concerns, gene editing technology holds significant potential for ensuring food security and promoting sustainable agriculture. Future research should further optimize gene editing methods and explore crop varieties that are adapted to diverse environmental conditions, thereby providing innovative solutions and scientific support for combating global climate change. This paper aims to comprehensively assess the applications, practical implications, and prospects of gene editing technology in regulating crop growth cycles to address global climate change.

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Soil moisture is a key environmental factor influencing nitrogen supply and release, playing a crucial role in crop growth and agricultural productivity. This study systematically reviews the mechanisms of water regulation in soil nitrogen transformation, mineralization, and loss processes (such as leaching and volatilization), clarifying the effects of water dynamics on nitrogen bioavailability and transport characteristics. By integrating water management techniques such as precision irrigation and alternate wetting and drying, the potential pathways for optimizing nitrogen supply and their role in improving nitrogen use efficiency are analyzed. The findings indicate that proper water management not only activates soil microbial activity and promotes efficient organic nitrogen transformation but also effectively suppresses greenhouse gas emissions such as nitrous oxide, thereby achieving the dual goals of efficient nitrogen utilization and environmental protection. Further exploration of water-nitrogen co-management strategies suggests that a scientifically integrated water and fertilizer management approach can significantly reduce agricultural non-point source pollution and optimize resource use efficiency. This study aims to provide theoretical foundations and practical pathways for sustainable agricultural production and environmental management by elucidating the coupling mechanisms of water regulation and nitrogen cycling, offering insights into resource-efficient utilization and green agricultural development.

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Wheat, one of the most important food crops globally, has long been threatened by various diseases, including rust and powdery mildew, which pose a significant challenge to food security. To address this issue, breeding wheat varieties with broad-spectrum and durable disease resistance has become a core goal in research and breeding. This paper comprehensively summarizes the latest progress and application trends in wheat disease resistance gene mining and functional research. First, it explains the pathogenic characteristics, genetic basis, and biological traits of disease resistance for major wheat diseases. Then, it systematically reviews the application and advantages of modern genomics tools (such as genome-wide association studies, genome re-sequencing, transcriptome analysis, etc.) in disease resistance gene identification, as compared to traditional phenotypic selection. Following that, the latest technological advancements in disease resistance gene functional research are discussed, including gene editing, functional validation, disease resistance signaling pathway analysis, and molecular mechanism exploration. Finally, the application prospects of disease-resistance genes in wheat breeding are analyzed, with a particular focus on the potential of genome editing technologies (such as CRISPR-Cas systems) and transgenic technologies in developing disease-resistant varieties. This paper aims to provide a systematic reference for wheat disease resistance research and breeding strategies. It anticipates the future application potential of combining multi-gene resistance strategies with novel biotechnology to enhance crop disease resistance and breeding efficiency.

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In recent years, with the rapid development of multi-omics technologies such as genomics, transcriptomics, and metabolomics, significant progress has been made in understanding the metabolic regulatory mechanisms of maize under drought stress. As a global agricultural challenge, drought severely affects maize growth and productivity. Maize enhances cellular drought tolerance by accumulating osmotic regulatory substances (e.g., proline, soluble sugars, and polyamines) and activating antioxidant systems to mitigate drought-induced oxidative damage. Additionally, secondary metabolites (e.g., phenolics and flavonoids) play a crucial role in improving drought resistance. Drought signaling pathways (such as ABA signaling) and related gene regulatory networks, including transcription factors and miRNAs, provide multilayered regulatory mechanisms that govern these metabolic processes, exhibiting complexity and dynamic interactions. This review systematically summarizes the metabolic regulatory mechanisms of maize under drought conditions, focusing on the latest research progress in osmotic regulation, antioxidant system activation, and secondary metabolite regulation. Furthermore, integrating multi-omics data reveals the complexity of maize drought resistance metabolic networks. By summarizing and analyzing current research findings, this study aims to provide theoretical support for a deeper understanding of maize drought resistance mechanisms and offer scientific guidance for drought-resistant breeding and agricultural applications.

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Peanut, as an important oil crop, has its oil content and quality directly determining its economic value and industrial application potential. With the rapid development of molecular biology and synthetic biology, researchers have gained a deeper understanding of the complex regulatory mechanisms underlying peanut oil metabolism. However, traditional research methods face limitations in deciphering and optimizing these intricate processes due to the multilayered gene networks and dynamic multifactorial regulation involved in lipid metabolism. The rapid rise of artificial intelligence (AI) technologies has provided novel perspectives and technical tools for this field. Through AI-driven gene network modeling and metabolic pathway analysis, researchers can accurately identify metabolic bottlenecks and key gene regulatory nodes, integrating large-scale and multidimensional experimental data for comprehensive analysis. These technologies not only significantly enhance the efficiency of gene editing target selection but also improve the scientific rigor and feasibility of metabolic pathway optimization. Moreover, AI-powered feedback optimization strategies further accelerate the iterative process of experimental validation and model refinement. This review comprehensively summarizes the current applications and recent advances of AI technologies in peanut oil metabolism research, systematically analyzing their practical implementations and potential challenges in gene selection, metabolic network optimization, and experimental validation. Additionally, future research directions are proposed to explore how AI can be fully leveraged to overcome bottlenecks in lipid metabolism optimization, providing theoretical support and technological solutions for peanut genetic improvement and efficient oil production.

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Soil salinization and alkalization are major limiting factors in global agricultural production, posing severe threats to rice yield and quality. To address this challenge, research on the physiological and biochemical regulatory mechanisms of salt-alkali-tolerant rice has become a key area in agricultural science. This paper systematically reviews the adaptive response mechanisms of rice under salt-alkali stress, covering key aspects such as cell membrane stability, ion balance regulation, antioxidant defense systems, and signaling networks. First, the dynamic structure and functional regulation of the cell membrane play a central role in salt-alkali tolerance, where the synergistic action of membrane lipids and proteins maintains cellular integrity and material transport functions. Second, by regulating sodium-potassium ion balance through selective ion channels and transporters, rice sustains normal metabolic activities under salt stress. Additionally, the cooperative action of antioxidant enzymes and non-enzymatic antioxidants alleviates oxidative damage caused by reactive oxygen species. Hormonal signaling pathways and complex transcription factor networks also play crucial roles in rice responses to salt-alkali stress. Finally, this paper explores the potential and practical applications of gene-editing technology and rhizosphere microorganisms in enhancing rice salt-alkali tolerance. By summarizing the latest research progress, this paper aims to reveal the intricate regulatory mechanisms of salt-alkali-tolerant rice, providing scientific references for molecular breeding and agricultural practices.

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Floating rice cultivation, as an innovative form of aquatic agriculture, has demonstrated significant potential in recent years for mitigating flood-related disasters, utilizing marginal water resources, and promoting climate-resilient agricultural transformation. This paper systematically summarizes the research progress in this field, tracing the technological evolution, ecological adaptation mechanisms, and future development directions. It first examines trends in optimizing floating platform materials and structures, the genetic improvement of suitable rice varieties, and the diversification of cultivation management systems. It then focuses on the ecological coupling characteristics of floating systems, discussing their environmental interactions and potential ecological risks from perspectives such as aquatic ecological adaptation, water quality dynamics, and climate resilience. Furthermore, the paper assesses the integration of digital agriculture and intelligent control technologies within the system, including environmental sensing, big data-driven cultivation models, automated management, and decision support. A comparative analysis of representative global case studies reveals regional differences and trends in technological adaptability, diffusion pathways, and potential for international cooperation. Finally, the paper highlights urgent scientific challenges and key areas for future research, including elucidating the three-dimensional coupling mechanisms among platforms, crops, and water bodies; advancing the integration of intelligent systems; achieving precise ecological niche matching in variety selection; and conducting multi-scale assessments of socioeconomic impacts. This paper aims to construct a systematic theoretical framework and technological roadmap for floating rice cultivation, providing both theoretical support and practical guidance for building resilient and sustainable agricultural systems.

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