Deep learning in cropland field identification: A review

文献类型： 外文期刊

第一作者： Xu, Fan

作者： Xu, Fan;Yao, Xiaochuang;Zhang, Kangxin;Feng, Quanlong;Yan, Shuai;Gao, Bingbo;Yang, Jianyu;Zhang, Chao;Zhu, Dehai;Yao, Xiaochuang;Feng, Quanlong;Gao, Bingbo;Yang, Jianyu;Zhang, Chao;Zhu, Dehai;Yang, Hao;Li, Ying;Li, Shaoshuai;Lv, Yahui;Ye, Sijing

作者机构：

关键词： Cropland field identification; Deep learning; Remote sensing; Bibliometric analysis; Sample dataset

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：7.7； 五年影响因子：8.4 ）

ISSN： 0168-1699

年卷期： 2024 年 222 卷

页码：

收录情况： SCI

摘要： The cropland field (CF) is the basic unit of agricultural production and a key element of precision agriculture. High-precision delineations of CF boundaries provide a reliable data foundation for field labor and mechanized operations. In recent years, with the dual advancements in remote sensing satellite technology and artificial intelligence, enabling the extraction of CF information on a wide scale and with high precision, research on CF identification based on deep learning (DL) has emerged as a highly esteemed direction in this field. To comprehend the developmental trends within this field, this study employs bibliometric and content analysis methods to comprehensively review and analyze DL research in the field of CF identification from various perspectives. Initially, 93 relevant literature pieces were retrieved and screened from two databases, the Web of Science Core Collection and the Chinese Science Citation Database, for review. The previous studies underwent quantitative analysis using bibliometric software across five dimensions: publication year, literature type and publication journal, country, author, and keyword. Subsequently, we analyze the current status and trends of employing DL in the field of CF identification from four perspectives: remote sensing data sources, DL models, types of CF extraction results, and sample datasets. Simultaneously, we combed through current publicly available sample datasets and data products that can be referenced to produce sample datasets for CFs. Finally, the challenges and future research focus of DL-based CF identification research are discussed. This paper provides both qualitative and quantitative analyses of research on DL-based CF identification, elucidating the current status, development trends, challenges, and future research focuses.

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