Advances in Resources Research

AI and Agriculture

公開日: 2025/02/18 更新日: 2025/02/18

8件中1～8件の論文を表示しています

5 巻 (2025) 1 号 p. 146-166 公開日: 2025/02/18
The application of artificial intelligence and gene editing technologies in high-yield crop breeding: Innovations and prospects for future agriculture
Xiaoyan Chen, Xintian Cui, Huatao Ma

With the growing global population and the increasing challenges of climate change on agricultural production, developing high-yield and resilient crop varieties has become increasingly urgent. Integrating artificial intelligence (AI) and gene editing technologies offers a groundbreaking solution to this issue. This article systematically explores the application of AI and gene editing in high-yield crop breeding, covering areas such as genome big data analysis, precise gene editing, phenotype prediction, resource optimization, and environmental adaptability assessment. Through case studies, the paper demonstrates the successful application of these technologies in key crops like rice and wheat. It provides an in-depth discussion of their continual improvement and future development trends. This study aims to offer new insights into agricultural innovation and proposes potential solutions for global food security and sustainable agricultural development.
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5 巻 (2025) 1 号 p. 235-254 公開日: 2025/02/18
Artificial intelligence-driven gene editing and crop breeding: Technological innovations and application prospects
Guokai Chen, Fuai Hao, Xiaojun Sun

The continuous growth in global food demand poses significant challenges for agriculture. Gene editing technology offers new solutions for crop breeding, yet the complexity of genome and multi-omics data interpretation remains a critical bottleneck. In recent years, the remarkable performance of artificial intelligence (AI) in big data processing and analysis has brought new opportunities to gene editing and crop breeding. This paper systematically explores the multi-level applications of AI in agricultural genomics, including data processing and analysis, gene-editing target prediction, crop phenotype prediction, breeding optimization, and automated high-throughput screening. Through case studies, the paper demonstrates how AI enhances the efficiency and accuracy of gene editing, shortens breeding cycles, and examines future development potential and challenges. This work aims to provide reference and inspiration for research and practical applications in related fields.
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5 巻 (2025) 1 号 p. 255-278 公開日: 2025/02/18
The research on dynamic monitoring and precise fertilization model of rice nitrogen demand based on big data and artificial intelligence: Innovative applications of real-time tracking and intelligent decision-making
Xiaoyu Zhao, Yaqing Li

In modern agricultural technology development, precision fertilization based on big data and artificial intelligence (AI) has become a critical strategy for enhancing rice production efficiency and achieving sustainable agriculture. This paper systematically analyzes the dynamic nitrogen demand of rice at various growth stages, proposing a data-driven precision fertilization model through real-time monitoring to optimize nitrogen application strategies and improve nitrogen use efficiency. By integrating soil sensors, the Internet of Things, and remote sensing technology, nitrogen demand can be monitored in real-time and undergo intelligent decision-making via AI algorithms, reducing nitrogen waste and minimizing environmental pollution. Additionally, the paper explores the potential of gene editing technology to enhance rice nitrogen use efficiency. Through multiple case studies, this paper demonstrates the practical applications of big data and AI technologies in rice nitrogen management, aiming to explore how technological innovations can advance intelligent nitrogen management and sustainable agricultural production.
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4 巻 (2024) 2 号 p. 171-180 公開日: 2025/02/18
Prospects of artificial intelligence applications in future agriculture
Xiaoling Li

With the rapid development of artificial intelligence (AI) technology, its widespread application across various industries has garnered considerable attention. This article aims to explore the potential applications and impact of AI in future agricultural production. It begins by introducing the successful applications of AI in different domains, providing readers with a contextual understanding. Subsequently, it reviews traditional agricultural production models, emphasizing the positive influence of AI on agricultural practitioners and global food supply, highlighting the challenges and opportunities faced by the agricultural sector. Furthermore, it delves into the key components of smart agriculture and their potential roles in soil monitoring, crop forecasting, and enhancing production efficiency. Finally, in conjunction with the discussion on the potential applications of AI in agriculture, the article underscores the prospective contributions of AI to future agricultural sustainability and global food security. Through this comprehensive exploration, the article aims to provide readers with a profound understanding of the prospects of AI in future agriculture.
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4 巻 (2024) 2 号 p. 231-251 公開日: 2025/02/18
Artificial intelligence in soil management: The new frontier of smart agriculture
Xiaoshu Zhang, Ping Yang, Baifang Lu

In recent years, the application of Artificial Intelligence (AI) in the agricultural sector has fundamentally transformed traditional agricultural practices, particularly in soil management. This article explores various applications of AI in soil management and its role in driving smart agriculture. By analyzing existing literature and case studies, this article elucidates the potential of AI technologies such as machine learning, remote sensing, and robotics in optimizing soil health, increasing crop yields, and reducing environmental risks. Furthermore, it discusses the challenges and opportunities faced in the widespread application of AI in soil management and proposes strategies to maximize its advantages in agriculture. This article aims to provide more scientific and intelligent solutions for agricultural production, thereby promoting sustainable agricultural development.
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4 巻 (2024) 3 号 p. 381-401 公開日: 2025/02/18
Pest and disease management in agricultural production with artificial intelligence: Innovative applications and development trends
Chunhua Li, Meihong Wang

This paper reviews the innovative applications and development trends of artificial intelligence (AI) in pest and disease management in agricultural production. As the scale of the agricultural output continues to expand and the complexity of environmental changes increases, the threat of pests and diseases to crops is becoming more prominent. The rapid development of AI technology offers new possibilities for addressing this issue. Firstly, the paper introduces the applications of AI in pest and disease identification and prediction, including the use of image recognition technology, data analysis, and machine learning algorithms, as well as the design and implementation of intelligent early warning systems. Secondly, it discusses the role of intelligent decision support systems in pest and disease management, covering the architecture of data-driven decision support systems, the implementation of intelligent agricultural management platforms, and the establishment of real-time monitoring and response mechanisms. Next, the paper explores the innovative applications of automated control technologies in pest and disease management, such as automated spraying and fertilization systems, intelligent agricultural robots, and the integration of biological control technologies with AI. Through case studies and practical applications, this paper showcases the successful application of AI in agricultural pest and disease management and provides an outlook on future development trends. This paper aims to provide agricultural producers and researchers with a comprehensive understanding, thereby promoting the further application and development of AI in agricultural pest and disease management.
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4 巻 (2024) 4 号 p. 624-653 公開日: 2025/02/18
The innovative applications and prospects of artificial intelligence in agricultural research: Gene editing, crop improvement, and intelligent data analysis
Beifang Liu, Hongtao Jiang, Lili Li

With the growth of the global population and the impact of climate change, agricultural research faces unprecedented challenges. Artificial intelligence (AI), with its powerful data processing and analytical capabilities, is rapidly transforming traditional methods in agricultural research. This article explores the applications and developments of AI in agricultural research, focusing on three key areas: gene editing, crop improvement, and data analysis. First, the article introduces the foundational AI technologies, including machine learning, data analysis, computer vision, and robotics. It then discusses in detail the application of AI in gene editing, highlighting how AI can enhance the efficiency and accuracy of gene editing processes. Next, the article examines the role of AI in crop improvement, where AI algorithms optimize gene selection and predict crop performance. Additionally, the application of AI in agricultural data analysis is thoroughly explored, with a focus on data-driven decision support and the implementation of precision agriculture. The use of AI in pest and disease monitoring, control, and adaptation to climate change is also addressed in depth. Finally, the article summarizes the current challenges AI faces in agricultural research applications and offers insights into future development directions. This article aims to provide researchers and practitioners with a comprehensive perspective on how AI is driving innovation and progress in agricultural research.
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4 巻 (2024) 4 号 p. 668-680 公開日: 2025/02/18
Applications and recent advances in artificial intelligence for soil data analysis
Kunxu Lu, Weisi Yan, Huilan Tan

As global agricultural modernization accelerates, the importance of soil data analysis in improving crop yields, optimizing resource use, and protecting the environment has become increasingly evident. This article explores the applications and development trends of Artificial Intelligence (AI) in soil data analysis. First, it introduces methods for soil quality monitoring using sensor technology and the role of AI in processing and analyzing this data to achieve real-time soil condition assessments. Next, it discusses AI-based intelligent fertilizer management, highlighting how AI provides precise fertilization recommendations and optimizes fertilizer use efficiency. The article then analyzes the application of AI in soil health prediction, including long-term monitoring, trend analysis, and intelligent decision support. Finally, it looks ahead to the future development trends of AI in this field, analyzing the challenges and opportunities it faces. The application of AI in soil data analysis has not only brought significant transformations to agricultural production but also offers new pathways for achieving sustainable agricultural development.
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