UAS-based remote sensing for agricultural Monitoring: Current status and perspectives

• Published in: Computers and electronics in agriculture 2024-12, Vol.227, p.109501, Article 109501
• Main Authors: Wang, Jingzhe, Zhang, Silu, Lizaga, Ivan, Zhang, Yinghui, Ge, Xiangyu, Zhang, Zipeng, Zhang, Wei, Huang, Qiujun, Hu, Zhongwen
• Format: Article
• Language: English
• Subjects: Agricultural monitoring; Growth condition; Precision agriculture; Remote sensing; Unmanned aerial system (UAS)
• ISSN: 0168-1699
• DOI: 10.1016/j.compag.2024.109501

•Survey on the Unmanned aerial system (UAS) applied in agricultural monitoring.•An analysis on bibliometric insights in UAS-based agricultural monitoring.•An overview of employed UAS-borne sensors and assessment methods.•Detailed applications and performance in various agricultural monitoring scenarios.•Limitations and potential perspectives in UAS-based agricultural monitoring. Timely and accurate acquisition of field crop growth and environmental information is essential for effective and accurate crop management. Low-altitude unmanned aerial system (UAS) remote sensing technology offers unparalleled advantages in obtaining crop images of different scales making it a vital tool for agricultural information monitoring. Additionally, machine learning (ML) and deep learning (DL) have significantly enhanced the efficiency and potential of UAS, especially for agricultural monitoring. This review aims to provide a comprehensive analysis by integrating bibliometric insights and thematic exploration to deepen our understanding of the research landscape and guide future investigations. We check commonly used UAS types and sensors, summarizing the advantages of UAS remote sensing in agricultural monitoring. Furthermore, critical examinations of the shortcomings observed in current research were also discussed. To further advance and optimize technology, analysis and monitoring methods, we identify crucial future research needs and directions: (1) Advancements in equipment and application scenarios, (2) Enhanced exploration of UAS multi-source data fusion, (3) Developments in methodology for agricultural monitoring, and (4) Expanding the scope of remote sensing applications in agricultural monitoring. The first two advancements will provide sufficient discriminative information, while the latter improvements will enhance the efficiency and generalizability of ML and DL methods for processing multi-platform remote sensing data. These developments will broaden the application of low-altitude UAS remote sensing in precision agriculture for improved crop information acquisition and analysis.