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The using of bibliometric analysis to classify trends and future directions on “smart farm”

机译:生物毛管测量分析对“智能农场”进行分类趋势和未来方向

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Climate change has affected the cultivation in all countries with extreme drought, flooding, higher temperature, and changes in the season thus leaving behind the uncontrolled production. Consequently, the smart farm has become part of the crucial trend that is needed for application in certain farm areas. The aims of smart farm are to control and to enhance food production and productivity, and to increase farmers' profits. The advantages in applying smart farm will improve the quality of production, supporting the farm workers, and better utilization of resources. This study aims to explore the research trends and identify research clusters on smart farm using bibliometric analysis that has supported farming to improve the quality of farm production. The bibliometric analysis is the method to explore the relationship of the articles from a co-citation network of the articles and then science mapping is used to identify clusters in the relationship. This study examines the selected research articles in the smart farm field. The area of research in smart farm is categorized into two clusters that are soil carbon emission from farming activity, food security and farm management by using a VOSviewer tool with keywords related to research articles on smart farm, agriculture, supply chain, knowledge management, traceability, and product lifecycle management from Web of Science (WOS) and Scopus online database. The major cluster of smart farm research is the soil carbon emission from farming activity which impacts on climate change that affects food production and productivity. The contribution is to identify the trends on smart farm to develop research in the future by means of bibliometric analysis.
机译:气候变化影响了所有国家的培养,洪水淹没,温度较高,季节的变化,从而留下了不受控制的生产。因此,智能农场已成为在某些农田中申请所需的关键趋势的一部分。智能农场的目标是控制和提高粮食生产和生产力,并增加农民的利润。应用智能农场的优势将提高生产质量,支持农业工人,以及更好地利用资源。本研究旨在利用养殖分析探讨研究趋势和识别智能电场研究集群,这些分析支持农业,以提高农业生产质量。 Bibliometric分析是探索文章的共同网络的文章关系的方法,然后用于识别关系中的集群。本研究审查了智能农田中所选的研究文章。智能农场的研究领域被分为两种群集,通过使用VosViewer工具与智能农场,农业,供应链,知识管理,可追溯性相关的关键字,使用VosViewer工具,从农业活动,粮食安全和农业管理的土壤碳排放。从科学(WOS)和Scopus在线数据库的产品生命周期管理和产品生命周期管理。智能农场的主要集群是农业活动的土壤碳排放,影响影响粮食生产和生产力的气候变化。贡献是通过伯格计数分析来确定智能农场的趋势,以通过生学计量分析开发未来研究。

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