With the increasing complexity of geological conditions and the depletion of global resources, petroleum exploration and reservoir modeling face unprecedented challenges. This study systematically explores the innovative applications of big data technology and artificial intelligence (AI) to improve exploration success rates and optimize reservoir modeling. Firstly, it analyzes the multidimensional complexity of seismic data and its pivotal role in petroleum exploration, detailing the applications of big data in data preprocessing, feature extraction, and pattern recognition. Secondly, it highlights the potential of AI algorithms in automated geological structure identification, refined reservoir modeling, and predictive analysis, emphasizing how data-driven approaches address the limitations of traditional modeling methods. The study proposes an intelligent exploration strategy based on big data and AI technologies, integrating real-time data analysis with dynamic adjustments to enhance the scientific accuracy of decision-making. Through case studies, the effectiveness of these methods in practical applications is validated, with an in-depth discussion of their advantages and technical challenges. Finally, the study envisions the future of big data and AI technologies in the petroleum industry, underscoring their pivotal role in advancing intelligent and efficient exploration and development.

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As oilfield development enters a complex stage, water injection, as a key method for enhancing oil recovery, is often significantly influenced by the complex geological characteristics of reservoirs, making precise prediction and optimization challenging. This study proposes a reservoir geological response prediction model based on big data analysis to address this issue. A machine learning-based prediction model is developed to simulate and evaluate reservoir dynamics under different water injection strategies by systematically collecting and integrating extensive geological, production, and water injection-related data. The model development process involves key steps such as data preprocessing, feature engineering, algorithm selection, and parameter optimization. Results show that the model demonstrates high accuracy in predicting reservoir dynamic behavior and effectively guides the optimization of water injection strategies, thereby significantly improving oilfield development efficiency and economic benefits. Furthermore, this study explores challenges encountered during model development, including data quality, algorithm selection, and computational complexity, while also highlighting the broad application prospects of big data technology in oilfield management. This study provides theoretical support and practical guidance for formulating scientific water injection strategies, contributing to extending oilfield development cycles and enhancing resource utilization efficiency.

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As oilfield development extends to complex and low-permeability reservoirs, fracturing technology has become a core approach to enhancing oil and gas recovery rates. However, traditional methods for evaluating fracturing performance often fall short of meeting the efficiency and precision demands of modern oilfield development due to limited data samples and simplistic analytical tools. This paper systematically reviews the latest advances in using big data and artificial intelligence (AI) technologies for assessing fracturing effectiveness. It begins by analyzing the application scenarios of mainstream fracturing technologies and the limitations of traditional evaluation methods. It then highlights the role of big data in cleaning, integrating, and analyzing fracturing operation data, as well as the potential of AI models, such as machine learning and deep learning, in predicting and optimizing production enhancement outcomes. Through case studies and empirical research, the significant benefits of data-driven fracturing optimization in improving development efficiency and reducing costs are demonstrated. Finally, the challenges of current applications, such as insufficient algorithm robustness and uneven data quality, are discussed, along with possible directions for future research and applications. This paper provides robust support for the intelligent and data-driven advancement of fracturing technology, offering significant practical implications for the efficient development of oil and gas resources.

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As oilfield development progresses into deeper and more complex formations, sand intrusion has become one of the primary challenges affecting well production stability and lifespan. Traditional sand control technologies often fall short in addressing complex well conditions, while introducing artificial intelligence (AI) provides a novel approach to optimizing oilfield sand control. This paper systematically outlines the key technical framework of AI in oilfield sand control, including machine learning-based historical data analysis, real-time optimization design, and multivariable dynamic simulation. By leveraging data-driven predictive models, AI can rapidly identify sand control technology combinations suited for different well conditions. Through dynamic simulation and real-time parameter updates, sand control strategies can continuously adapt to changing well conditions, enhancing the scientific basis and decision-making effectiveness. Combined with practical case analyses, this paper further explores the practical effectiveness of AI in sand control and the associated technical and management challenges, offering new perspectives and technical support for oilfield production management. The aim is to establish a scientific and systematic AI-driven framework for optimizing oilfield sand control technology, providing guidance and technical references for achieving intelligent and efficient oilfield production management and promoting sustainable industry development.

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Microbial enhanced oil recovery (MEOR) utilizes microbial metabolic products to improve reservoir properties and enhance oil recovery, making it a key direction in tertiary oil recovery technologies. However, due to the complexity of reservoir environments and the dynamic mechanisms of microbial activity, traditional methods face significant limitations in optimizing injection strategies and improving recovery efficiency. This study integrates big data and artificial intelligence technologies to investigate numerical simulation and intelligent optimization methods for MEOR systematically. Firstly, the principles and technical challenges of MEOR are analyzed, highlighting the critical role of big data in reservoir data integration and the formulation of precise injection strategies. Secondly, dynamic numerical models of the microbial reservoir system are developed using machine learning and deep learning techniques to simulate the multi-layered impacts of microbial metabolism, injection parameters, and environmental conditions on oil recovery efficiency. Furthermore, optimization algorithms such as genetic algorithms and deep reinforcement learning are explored for their application in optimizing injection parameters, enabling intelligent and optimal decision-making support for injection strategies. Case studies in real oilfields demonstrate the significant advantages of big data-driven numerical simulations and intelligent optimization in improving MEOR efficiency. Finally, the future directions of MEOR are discussed, including data-driven multi-scale modeling, real-time optimization under complex reservoir conditions, and intelligent control. This study provides a novel practical pathway to advance MEOR technologies.

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Intelligent water injection control systems are critical for efficient management in modern oilfield development. Leveraging big data and artificial intelligence (AI), these systems enable real-time monitoring, dynamic optimization, and adaptive control of the water injection process. This paper provides a comprehensive review of the key technologies and advancements in intelligent water injection control systems, including decision support methods based on big data analysis, the application of AI algorithms in injection optimization, and integrating sensor networks with Internet of Things technologies. Through the study of typical application cases, the significant benefits of these systems in enhancing oil recovery, reducing operational costs, and extending reservoir development life are highlighted. The challenges in current technological development, such as data quality, model robustness, and field adaptability, are discussed. The paper also explores the future potential of intelligent oilfields toward higher levels of automation and intelligence. This paper offers valuable insights for both theoretical research and practical applications, aiming to drive technological innovation and industrial upgrades in oilfield development.

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As a promising new energy resource, natural gas hydrates are important for energy exploration and development by studying their geological distribution. The rapid growth of big data technologies has provided new perspectives and technical support for this field in recent years. This paper systematically reviews the application of big data technologies in the study of natural gas hydrate geological distribution, covering recent advancements in data integration and management, machine learning, and predictive modeling, as well as spatiotemporal analysis and visualization. By analyzing case studies from typical global hydrate distribution regions, the paper highlights the effectiveness and potential of big data technologies in predicting and validating hydrate distribution. Addressing key challenges in current research, such as data acquisition, quality control, and multidisciplinary collaboration, the paper proposes potential solutions and outlines future technological development directions. This paper aims to provide a reference for advancing research on the geological distribution of natural gas hydrates and to support scientific decision-making in energy development and utilization.

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As the complexity and scale of oilfield development continue to grow, equipment condition monitoring and fault prediction play a crucial role in ensuring production safety and enhancing operational efficiency. In recent years, significant progress has been made in applying big data and artificial intelligence (AI) technologies in this field, enabling more accurate condition assessment and fault warning capabilities. This paper systematically reviews the latest research advances in oilfield equipment condition monitoring and fault prediction, focusing on key methods, implementation processes, and real-world application cases based on big data and AI technologies. Specifically, it explores the core principles of condition monitoring, AI-driven predictive models, and the performance of related technologies in industrial scenarios. Moreover, it provides an in-depth analysis of current challenges, including data quality issues, the generalization ability of AI models, limitations in real-time performance, and interpretability. It also discusses future development trends, such as integrating edge computing and digital twin technologies to enhance system intelligence. By thoroughly summarizing existing achievements and challenges, this paper provides theoretical foundations and technical references for further research and optimization of oilfield equipment monitoring and fault prediction systems.

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The Da’anzhai Member of the Jurassic in the Sichuan Basin is an important potential area for lacustrine shale gas resources in China, with significant exploration and development prospects. Based on systematic sampling and testing, this study comprehensively analyzed the lithofacies characteristics, organic matter properties, physical parameters, and formation conditions of the shale in this member. It investigated the main controlling factors of shale gas enrichment. The results show that the Da’anzhai Member shales exhibit significant heterogeneity, with TOC content ranging from 0.1% to 3.9%, primarily consisting of Type Ⅱ2 and Type Ⅲ organic matter. The Ro values range from 1.1% to 1.8%, indicating that the shale is mature to highly mature. Porosity ranges from 0.9% to 8.4%, dominated by micropores and mesopores, and the average gas content reaches 0.9 m³/t, demonstrating good reservoir performance. Shale gas enrichment is primarily controlled by a combination of a semi-deep lacustrine depositional environment, favorable lithofacies assemblages, well-developed natural fractures, and effective preservation conditions. Based on this research, evaluation criteria for favorable shale gas zones were established, and five high-quality exploration areas were identified, including the southwestern Yuanba, southeastern Langzhong, Yilong, northwestern Fuling, and northwestern Jiannan regions. This study elucidates the formation mechanism and enrichment patterns of shale gas in the Da’anzhai Member of the Jurassic in the Sichuan Basin, providing scientific evidence and technical support for the efficient exploration and development of lacustrine shale gas resources.

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The accuracy of power load forecasting is a critical factor in ensuring the stability and operational efficiency of power systems. With the rapid advancement of big data and artificial intelligence technologies, these innovations offer novel methods and efficient tools for power load forecasting. This paper provides a systematic review of the application of big data and artificial intelligence in power load forecasting, thoroughly comparing the advantages and limitations of traditional methods and modern techniques, and analyzing the key technical challenges faced today. It focuses on the roles of data preprocessing, feature engineering, and advanced algorithms in optimizing load forecasting and demonstrates the practical effectiveness of these technologies in smart grids through typical case studies. Finally, the paper explores future development trends, identifies key areas for further research, and proposes strategic recommendations. This paper offers theoretical foundations and practical insights for power system planners, researchers, and policymakers, aiming to advance the development of smart grids and optimize power systems.

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With the deepening digital transformation of power systems and the integration of high proportions of renewable energy, the operational environment of power grids has become increasingly complex, posing unprecedented challenges to safety and reliability. Traditional safety risk assessment and early warning methods have limitations in addressing dynamic system changes and nonlinear characteristics, whereas the application of big data and artificial intelligence (AI) technologies provides new approaches and tools for intelligent safety management of power systems. This paper systematically reviews the current state of research on safety risk assessment in power systems, focusing on big data-driven risk quantification methods and AI-enhanced fault prediction mechanisms. It proposes a design framework and implementation path for intelligent early warning systems. Through practical case studies, this paper analyzes typical application scenarios of these technologies, such as equipment health monitoring, extreme weather forecasting, and cybersecurity protection. Finally, the paper summarizes key challenges in smart grid development, including data privacy protection, model interpretability, and limited real-time responsiveness, and outlines future research directions. By exploring the integrated application of big data and AI technologies in power system safety risk assessment, this paper aims to provide theoretical foundations and technical support to enhance the safety and reliability of smart grids, contributing to stable operation and sustainable development of power systems.

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With the global adoption of electric vehicles (EVs), their interaction with power systems has become a key research area in energy transition and smart grid development. Intelligent EVs are not only consumers of electricity but can also support power systems through technologies such as Vehicle-to-Grid, offering regulation capacity and flexibility. This paper systematically reviews the core technologies enabling intelligent EV and power system interaction, including smart charging and discharging management, Vehicle-to-Grid and Vehicle-to-Everything technologies, distributed energy storage integration, frequency regulation, and demand response services. It further analyzes the primary challenges facing current technological development, such as impacts on battery lifespan, lack of standardized protocols, adaptability of infrastructure, and cybersecurity risks. Additionally, the evolution of global policy frameworks and market incentive mechanisms is discussed, drawing insights from typical application cases and pilot projects. Finally, the paper envisions the future integration of intelligent EVs with power systems, emphasizing the potential of smart grid construction, renewable energy optimization, and the empowerment of big data and artificial intelligence. By comprehensively reviewing current research achievements and technological bottlenecks, this paper aims to provide scientific guidance for advancing intelligent EV and power system interaction, fostering innovation and practical applications in this field.

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In the face of increasingly severe global agricultural challenges, enhancing crop yield, disease resistance, and stress tolerance has become a research priority. The rapid advancement of artificial intelligence (AI) technologies offers new opportunities for crop gene research. This paper systematically reviews the latest applications of AI in crop genomics, focusing on its specific use cases in gene data processing, gene-trait association analysis, functional gene prediction, and molecular marker screening. Furthermore, the paper explores the potential of AI in assisting crop breeding, particularly in accelerating the development of high-yield, disease-resistant, and stress-tolerant varieties. The integration of AI technologies with traditional breeding methods and their potential applications are also discussed, along with the technical challenges and data bottlenecks currently faced in research. Finally, the paper envisions the future development of AI in crop gene research and provides several recommendations for future studies. By offering a comprehensive perspective on the role of AI in crop gene research, this review aims to support further advancements in the field.

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Wheat is a globally important food crop, and research on its drought and salinity tolerance is crucial for ensuring food security. In recent years, the rapid development of artificial intelligence (AI) technology in agriculture has provided innovative methods and tools to enhance wheat stress resistance. This paper systematically reviews the progress of AI applications in studying wheat drought and salinity tolerance, focusing on key areas such as genomic data analysis, phenotypic data interpretation, and the analysis of gene-environment interactions. The application of AI has enabled researchers to more efficiently identify key genes associated with drought and salinity tolerance, construct high-precision predictive models, and optimize breeding strategies. Moreover, developing intelligent breeding platforms and real-time monitoring systems has further deepened and broadened research on wheat stress resistance. Despite the promising prospects of AI in this field, challenges remain in improving model accuracy, fostering interdisciplinary collaboration, and promoting research outcomes. This paper aims to provide new insights into wheat drought and salinity tolerance research and explore future directions and priorities.

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Enhancing rice’s salt-alkali tolerance is important to agricultural production on globally salinized and alkaline soils. In recent years, the critical role of rhizosphere microorganisms in helping plants adapt to adverse conditions has drawn increasing attention, making the screening of core strains that enhance rice’s salt-alkali tolerance a research hotspot. This study integrates big data analysis and pattern recognition technologies with metagenomics and 16S rRNA sequencing to comprehensively uncover the diversity and functional profiles of rice rhizosphere microorganisms. Key microbial groups associated with salt-alkali tolerance were rapidly identified using machine learning and deep learning algorithms. AI-driven functional prediction and optimization methods, combined with genomic information, were applied to evaluate the salt-alkali tolerance potential of candidate strains. Furthermore, a high-throughput screening platform was established, and field trials validated the growth-promoting effects of the selected strains on rice. Leveraging AI’s adaptive learning capabilities, application conditions were optimized, and the strains’ adaptability to various salt-alkali environments was predicted. This study aims to explore the potential of big data and AI technologies in rhizosphere microbial screening, building an efficient and intelligent system for strain selection and optimization, and providing innovative strategies and solutions to enhance rice’s salt-alkali tolerance.

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The complex interactions among soil, plants, and microbes in paddy field ecosystems are crucial for their productivity, health, and sustainability. With the rapid advancement of big data technologies, significant progress has been made in uncovering these interactions. This paper reviews the applications of big data technologies in studying soil-plant-microbe interactions in paddy fields, focusing on big data analysis methods related to soil's physical and chemical properties, plant growth dynamics, and microbial community structures. It analyzes the key factors influencing paddy ecosystem functions and their interaction patterns. Furthermore, the paper discusses precision agricultural management strategies driven by big data, including precision fertilization, water resource management, microbial community regulation, and integrated pest and disease control. Finally, it explores the future development trends of big data technologies in paddy ecosystem research, highlighting current technical challenges and the importance of interdisciplinary collaboration. This paper aims to provide theoretical insights and practical references for the sustainable development of paddy field management.

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In recent years, the rapid development of big data technology has brought profound changes to agriculture, especially in the research of rice disease resistance genes. This paper systematically reviews the application of big data technology in the mining and functional research of rice disease resistance genes. First, the basic concepts of big data technology and its current applications in agriculture are outlined. Then, the limitations of traditional research methods for studying major rice diseases and their resistance genes are analyzed. Subsequently, the critical roles of big data technology in key aspects of rice disease resistance gene research are discussed in detail, including data collection, cleaning, integration, standardization, and feature extraction. Moreover, the primary methods for gene mining are introduced, such as gene association analysis, genome-wide association studies, machine learning, and deep learning techniques. The paper further explores the techniques for functional prediction and validation of resistance genes, as well as methods for studying their molecular mechanisms. Through typical case studies, the specific applications of big data technology in researching resistance genes for major rice diseases, such as rice blasts and rice false smut, are demonstrated. Finally, the paper summarizes the challenges and future development directions of big data technology in rice disease resistance gene research. It emphasizes issues like data quality control, data sharing, and the complexity of gene functional studies, while envisioning the broad prospects of big data technology in future agricultural research. This paper provides valuable academic references for advancing research on rice disease resistance genes.

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With the intensifying impact of global climate change on agricultural production, drought-resistant breeding of maize, a vital food crop, has become a key area of international research. Long breeding cycles, high costs, and low efficiency constrain traditional breeding methods. The introduction of artificial intelligence (AI) has brought transformative breakthroughs to maize drought resistance breeding. This paper systematically reviews the primary applications of AI in this field, including genomic data analysis, high-throughput phenotypic data collection, construction of drought-resistance gene networks, and AI-driven strategies in marker-assisted breeding. By integrating multi-omics data and intelligent algorithms, AI significantly enhances the precision of gene selection and drought phenotype analysis. AI demonstrates broad potential in identifying gene-editing targets for drought resistance, phenotypic identification, and metabolic pathway analysis, providing strong support for precision breeding. This paper aims to summarize the latest advancements in AI technology in maize drought resistance breeding, analyze current challenges, and explore future directions for technological integration and innovation, offering new insights and strategies for maize drought resistance breeding.

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Peanut, as a globally important oil crop, has oil content as a key determinant of its economic value and market competitiveness. In recent years, rapid advances in genomics and multi-omics technologies have significantly advanced the understanding of the genetic basis and regulatory mechanisms underlying peanut oil content. Through genome-wide association studies, quantitative trait locus mapping, transcriptomics, and metabolomics, researchers have identified multiple key genes closely associated with lipid synthesis and accumulation. Additionally, gene-editing technologies such as CRISPR-Cas9 have provided new breeding tools for enhancing peanut oil content, while molecular marker-assisted selection has accelerated the development of high-oil-content varieties. Furthermore, the integration of big data and artificial intelligence in data mining, gene identification, and breeding optimization has opened new research avenues. Studies have also highlighted the critical role of gene-environment interactions in peanut lipid metabolism, providing theoretical support for optimizing cultivation practices. This paper systematically summarizes recent progress in genetic research on peanut oil content, explores the application of multi-omics and modern breeding technologies, and discusses future research directions and challenges. This paper aims to provide theoretical guidance for the genetic improvement of peanut oil content and to inspire further research in this field.

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Sweet potato (Ipomoea batatas), as a globally important food and economic crop, is widely cultivated for its high yield, broad environmental adaptability, and rich nutritional value. In recent years, the rapid development of gene editing technologies has brought new opportunities to sweet potato breeding, particularly in improving yield, enhancing disease and stress resistance, and optimizing nutritional quality. However, the complex hexaploid genome of sweet potato presents significant challenges for the application of gene editing. Current research primarily focuses on genome sequencing, disease-resistance gene analysis, and optimizing the CRISPR/Cas9 system. Breakthroughs in these areas have significantly improved disease resistance, drought tolerance, and nutritional composition in sweet potatoes. Furthermore, the advancement of molecular marker-assisted selection and multi-trait breeding technologies has further promoted the practical application of gene editing in sweet potato breeding. Despite notable progress, the widespread application of sweet potato gene editing still faces challenges related to regulation, ethics, and industrialization. This review summarizes the latest research progress in sweet potato gene editing and breeding technologies, analyzes key issues, and discusses future development trends to provide scientific insights for advancing sweet potato gene editing and breeding research.

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The interaction between soil and water plays a crucial role in agricultural ecosystems, profoundly impacting crop water uptake, nutrient absorption, root development, and overall physiological function. Soil properties such as physical structure, porosity, and organic matter content determine the efficiency of water infiltration, retention, and utilization, directly influencing crop drought resistance and growth quality. Scientific water management practices, such as precision irrigation and soil improvement, can effectively enhance water resource utilization and promote healthy crop growth under varying environmental conditions. This paper systematically elaborates on the interaction mechanisms between soil structure and water dynamics, providing an in-depth analysis of key processes related to water infiltration, retention, and utilization, while also proposing optimization strategies to improve water use efficiency. The study further discusses the adaptive performance of soil-water interactions in extreme environmental conditions such as drought and flooding. This study aims to reveal the core mechanisms of soil-water interaction and provide theoretical support for scientific water management, aiming to achieve sustainable agricultural development and healthy crop growth.

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The structure of soil aggregates is a crucial factor influencing crop growth, nutrient uptake, and water retention capacity, while roots acquire essential nutrients like nitrogen through dynamic interactions with soil structure. Optimizing soil physical properties has been proven an effective approach to improving nitrogen use efficiency; however, the complexity of soil-root interactions, involving multiple variables and nonlinear dynamics, poses challenges for traditional research methods to fully uncover the underlying mechanisms. To address this, this study employs artificial intelligence (AI) technology to develop a three-dimensional dynamic model of soil aggregate structure and root interaction, simulating root growth and nitrogen uptake characteristics under various soil physical conditions. The results demonstrate that different soil aggregate structures, such as soil looseness and organic matter content, significantly affect nitrogen uptake efficiency in roots, revealing the intricate relationships between soil physical properties and nitrogen cycling. The model further provides theoretical insights and practical guidance for optimizing organic fertilizer application strategies, enhancing soil water retention and aeration, and promoting efficient nitrogen uptake. By integrating AI-driven dynamic modeling techniques, this study offers innovative approaches to understanding the coupling mechanisms between soil physical properties and crop nitrogen uptake, contributing to precision agriculture and sustainable soil management.

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Soil water retention capacity is a critical factor influencing farmland productivity, the sustainability of agricultural ecosystems, and resource use efficiency. Due to significant variations in water retention performance among different soil types, optimizing soil improvement measures to enhance this capacity is very important. This study systematically analyzes the mechanisms of various soil improvement techniques, including the addition of organic materials such as biochar and compost, as well as the effects of water-retaining agents and novel materials on soil structure and water retention. By integrating machine learning methods, a model was developed to precisely evaluate the water retention effects of these improvement measures, along with a data-driven intelligent recommendation system that provides tailored solutions for different soil types and environmental conditions. The findings demonstrate that the application of artificial intelligence in soil management improves the efficiency and accuracy of improvement techniques and provides scientific support for sustainable agricultural production. This study offers practical insights for advancing innovations in soil improvement technologies and promoting intelligent agricultural management.

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Soil microbial communities play a central role in the nitrogen cycle, and their function directly impacts crop nitrogen use efficiency. With the rapid advancement of multi-omics technologies such as metagenomics and metabolomics, the ability to reveal the composition, function, and dynamic changes of microbial communities has significantly improved. This study integrates metagenomics and metabolomics to systematically explore the effects of organic fertilizer application and water regulation on soil microbial community structure and function, and to elucidate their mechanisms in nitrogen cycle regulation. The results show that organic fertilizer significantly reshaped the microbial community composition and enhanced the expression of functional genes related to nitrogen transformation. Water regulation, on the other hand, further influenced nitrogen availability and mobility by modulating microbial metabolic activities. Based on large-scale integration analysis of multi-omics data, this study constructed a complex network between microbial communities and the nitrogen cycle, revealing the response patterns of microbes under different management practices and their contributions to nitrogen use efficiency. This study provides new insights into the complex relationship between soil microbes and nitrogen cycling, offering scientific support for optimizing agricultural management practices to improve nitrogen use efficiency.

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With the expansion of agricultural production scale and the intensification of farming practices, the threats posed by pests and crop yield and quality diseases have become increasingly severe. Traditional pest and disease monitoring and control methods often fail to meet modern agriculture’s demands for efficiency and precision due to issues such as delayed detection and excessive pesticide use. This paper systematically analyzes the current applications and development trends of artificial intelligence (AI) technologies in pest and disease monitoring and control in agriculture. Leveraging image recognition technology, AI enables efficient identification and automated monitoring of crop diseases and pests, supporting real-time early warning systems. Combined with big data analysis, AI can also provide precise control strategies, optimize pesticide usage, and reduce environmental pollution. Additionally, the potential of AI in biological control and organic farming is noteworthy, contributing to sustainable agricultural development. Although AI faces challenges in technical, ethical, and social dimensions in pest and disease monitoring and control, its development potential remains immense. This paper aims to explore the technical principles, practical applications, and future directions of AI in agricultural pest and disease monitoring and control, providing insights for further research and application in this field.

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In modern agriculture, the extensive use of pesticides and fertilizers has significantly increased crop yields but has also led to severe environmental pollution and ecological crises. With the rapid advancement of gene editing technology, innovative solutions for promoting sustainable agricultural development have emerged. This paper systematically explores the potential applications of gene editing technology in reducing the use of pesticides and fertilizers and thoroughly analyzes its positive impacts on the agricultural environment. By editing genes related to nitrogen use efficiency, the application of nitrogen fertilizers can be significantly reduced, thereby mitigating the risk of water and soil pollution. Additionally, gene editing technology enhances crop resistance to pests and diseases, fundamentally decreasing pesticide usage and minimizing its harmful effects on the ecosystem and human health. Furthermore, by improving crop adaptability to stress conditions such as drought or salinity, gene editing technology helps reduce reliance on fertilizers and irrigation resources. Despite its promising prospects in agriculture, the promotion of this technology faces multiple challenges, including technical bottlenecks, ethical controversies, and regulatory constraints. This paper reviews the latest research progress in the application of gene editing technology in agriculture, focusing on its potential value in environmental protection and sustainable agricultural development, providing theoretical support and reference for related research and practice.

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Water and fertilizer integration technology, as an important innovation in modern agriculture, combines irrigation and fertilization systems to regulate the supply of water and nutrients precisely, thereby improving agricultural production efficiency and resource utilization. This paper systematically elaborates on the fundamental principles of water and fertilizer integration technology, with a focus on analyzing its application effects under different crop and soil conditions, pathways for technological optimization and improvement, and a comprehensive assessment of its environmental and economic benefits. The research findings indicate that water and fertilizer integration technology offers significant advantages in water and fertilizer conservation, enhancing crop yield and quality, while also demonstrating great potential for efficient resource utilization and environmental protection. This paper further explores the technical, economic, and management challenges in promoting the application of this technology and, based on the latest research findings, outlines future development directions. The continuous innovation and promotion of water and fertilizer integration technology drive the green transformation and sustainable development of agriculture and present new opportunities and growth points for optimizing the global agricultural production system. This paper aims to provide theoretical references and practical guidance for researchers and agricultural practitioners, facilitating the in-depth application and promotion of water and fertilizer integration technology in modern agriculture.

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Water resources are a core element of agricultural production, with their utilization efficiency directly influencing crop growth and yield. However, as global climate change and population growth intensify, water scarcity has become increasingly severe, necessitating a scientifically effective management strategy to optimize water resource allocation. Against this backdrop, big data and artificial intelligence (AI) technologies offer innovative solutions for analyzing and optimizing the relationship between water resource utilization and crop yield. This study systematically examines the correlation between water usage and crop yield by integrating historical, climate, and crop yield data to develop predictive models based on machine learning and deep learning. The research further explores the impacts of different irrigation methods, crop varieties, and environmental conditions on water use efficiency. It applies AI technologies to design optimized water management strategies aimed at maximizing crop yields. The research demonstrates these technologies’ successful application and effectiveness in regional water resource management through case studies, providing technical support and scientific evidence for optimizing agricultural water strategies and increasing crop yields. The findings offer new pathways and methodologies for achieving sustainable agricultural development under limited water resource conditions.

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Drought is one of the major abiotic stresses affecting winter wheat production, significantly influencing material transport and yield formation, with different varieties exhibiting distinct drought resistance mechanisms. To explore the intrinsic relationship between material transport and yield response under different drought resistance strategies, this study investigated the conventional variety Dongying 22 and the drought-resistant variety Dongying 38 under drought stress (WS, 40%–45% of field capacity) and normal water conditions (CK, 80%–85% of field capacity) from jointing to maturity. The study systematically analyzed floret development dynamics, dry matter accumulation, and transport in vegetative organs, grain-filling characteristics, and key yield components. The results showed that drought stress significantly affected material transport and single spike yield in both varieties, but their response mechanisms differed. Dongying 22 mainly relied on the transport of pre-anthesis stored dry matter from vegetative organs to grains for grain filling, but drought stress significantly inhibited this transport, leading to reductions of 7.6%, 15.9%, and 24.3% in spikelet number, grain weight, and single spike yield, respectively. In contrast, Dongying 38 enhanced post-anthesis dry matter transport to grains, improving grain filling contribution and reducing dependence on pre-anthesis reserves, resulting in smaller decreases in spikelet number (3.3%), grain weight (9.5%), and single spike yield (16.3%). Additionally, Dongying 38 maintained stable floret development and showed no significant change in grain filling rate under drought stress, whereas the grain filling rate of Dongying 22 decreased by approximately 42%. These findings suggest that Dongying 38 exhibits stronger drought adaptation by optimizing dry matter accumulation and transport strategies under drought conditions. This study provides practical insights for drought-resistant breeding and efficient water management in winter wheat production.

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With the increasing global demand for efficient and sustainable agriculture, soybean, as a crucial economic crop, plays a key role in the food, feed, and industrial sectors. This paper systematically summarizes recent research progress in soybean production, focusing on genetic improvement and molecular breeding, pest and disease control with biotechnology, precision agriculture, and intelligent management, as well as environmental adaptation and sustainable development. The study highlights the applications of genomics, molecular marker technology, and CRISPR/Cas9 in cultivar development, analyzes integrated pest and disease management strategies combining traditional and biological control methods, and evaluates precision agriculture models supported by sensors, drones, remote sensing, big data, and artificial intelligence. Furthermore, it provides an in-depth discussion on soybean nitrogen fixation mechanisms, ecological farming models, and climate adaptation strategies, while also exploring emerging topics such as multi-omics integration, systems biology, smart agricultural equipment, and global policies and market trends. This paper aims to offer a comprehensive and systematic reference for researchers and agricultural policymakers, promoting the advancement of soybean production in a more scientific, intelligent, and environmentally sustainable direction.

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