Unveiling temporal and spatial research trends in precision agriculture: A BERTopic text mining approach
文献类型： 外文期刊
第一作者： Liu, Yang
作者： Liu, Yang;Liu, Yang;Wan, Fanghao
作者机构：
关键词： Precision agriculture; Agriculture technology; BERTopic; Text ming; Natural language processing
期刊名称：HELIYON （ 影响因子：3.6； 五年影响因子：3.9 ）
ISSN：
年卷期： 2024 年 10 卷 17 期
页码：
收录情况： SCI
摘要： This study leverages the BERTopic algorithm to analyze the evolution of research within precision agriculture, identifying 37 distinct topics categorized into eight subfields: Data Analysis, IoT, UAVs, Soil and Water Management, Crop and Pest Management, Livestock, Sustainable Agriculture, and Technology Innovation. By employing BERTopic, based on a transformer architecture, this research enhances topic refinement and diversity, distinguishing it from traditional reviews. The findings highlight a significant shift towards IoT innovations, such as security and privacy, reflecting the integration of smart technologies with traditional agricultural practices. Notably, this study introduces a comprehensive popularity index that integrates trend intensity with topic proportion, providing nuanced insights into topic dynamics across countries and journals. The analysis shows that regions with robust research and development, such as the USA and Germany, are advancing in technologies like Machine Learning and IoT, while the diversity in research topics, assessed through information entropy, indicates a varied global research scope. These insights assist scholars and research institutions in selecting research directions and provide newcomers with an understanding of the field's dynamics.
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