Mustafa Ghulam, Liu Yuhong, Khan Imran Haider, Hussain Sarfraz, Jiang Yuhan, Liu Jiayuan, Arshad Saeed, Osman Raheel
Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing, China.
College of Agriculture, Nanjing Agricultural University, Nanjing, China.
Front Plant Sci. 2024 Aug 9;15:1401246. doi: 10.3389/fpls.2024.1401246. eCollection 2024.
Recently, a rapid advancement in using unmanned aerial vehicles (UAVs) for yield prediction (YP) has led to many YP research findings. This study aims to visualize the intellectual background, research progress, knowledge structure, and main research frontiers of the entire YP domain for main cereal crops using VOSviewer and a comprehensive literature review. To develop visualization networks of UAVs related knowledge for YP of wheat, maize, rice, and soybean (WMRS) crops, the original research articles published between January 2001 and August 2023 were retrieved from the web of science core collection (WOSCC) database. Significant contributors have been observed to the growth of YP-related research, including the most active countries, prolific publications, productive writers and authors, the top contributing institutions, influential journals, papers, and keywords. Furthermore, the study observed the primary contributions of YP for WMRS crops using UAVs at the micro, meso, and macro levels and the degree of collaboration and information sources for YP. Moreover, the policy assistance from the People's Republic of China, the United States of America, Germany, and Australia considerably advances the knowledge of UAVs connected to YP of WMRS crops, revealed under investigation of grants and collaborating nations. Lastly, the findings of WMRS crops for YP are presented regarding the data type, algorithms, results, and study location. The remote sensing community can significantly benefit from this study by being able to discriminate between the most critical sub-domains of the YP literature for WMRS crops utilizing UAVs and to recommend new research frontiers for concentrating on the essential directions for subsequent studies.
近年来，利用无人机进行产量预测（YP）取得了快速进展，产生了许多产量预测研究成果。本研究旨在通过VOSviewer和全面的文献综述，可视化主要谷类作物整个产量预测领域的知识背景、研究进展、知识结构和主要研究前沿。为了构建用于小麦、玉米、水稻和大豆（WMRS）作物产量预测的无人机相关知识可视化网络，从科学网核心合集（WOSCC）数据库中检索了2001年1月至2023年8月发表的原始研究文章。研究发现了产量预测相关研究发展的重要贡献者，包括最活跃的国家、多产的出版物、高产的作者、贡献最大的机构、有影响力的期刊、论文和关键词。此外，该研究观察了无人机在微观、中观和宏观层面上对WMRS作物产量预测的主要贡献以及产量预测的合作程度和信息来源。此外，在对资助和合作国家的调查中发现，中华人民共和国、美利坚合众国、德国和澳大利亚的政策援助极大地推动了与WMRS作物产量预测相关的无人机知识。最后，展示了关于WMRS作物产量预测的数据类型、算法、结果和研究地点的研究结果。遥感界可以从这项研究中受益匪浅，可以区分利用无人机进行WMRS作物产量预测文献中最关键的子领域，并为后续研究的重点方向推荐新的研究前沿。
