A review of key technologies in variable-rate spreading using unmanned aerial systems (UAS)
The variable-rate spreading technology (VRST) utilizing agricultural Unmanned Aerial Systems (UAS) has become a crucial element of modern agriculture due to its highly efficient and precise operational capabilities. This study focuses on current advancements in UAS-based VRST. First, at the actuator level, it describes the structural principles and technological features of uniform and row-based spreading systems, analyzing the function of gas-solid coupling technology as a key research method in agricultural applications. Second, at the control system level, it summarizes two decision-making modes: offline prescription maps and online real-time sensing. It also examines the current level of research on flow monitoring technologies, intelligent control algorithms, and latency compensation techniques for systems. Finally, suggestions for future improvement are proposed to address present issues such as poor environmental adaptability, low multi-source information fusion, and significant system latency. These include constructing dynamic application prediction models, developing machine-tool collaborative control systems, and establishing standardized operational protocols. These efforts aim to lay the groundwork for the research, development, and application of VRST and equipment.
Authors
- Yan Xu (ORCID: https://orcid.org/0000-0003-0379-8866)
- Shuning Si (ORCID: https://orcid.org/0000-0002-9619-7092)
- Yue Jin (ORCID: https://orcid.org/0009-0008-3785-337X)
- Meng Yu (ORCID: https://orcid.org/0000-0003-2554-2888)
- Junzhe Qu
- Xinyu Xue
- Suming Ding
- Li Tian
Institutions
- Chinese Academy of Agricultural Engineering (CN)
- Chinese Academy of Agricultural Sciences (CN)
- Ministry of Agriculture and Rural Affairs (CN)
- Nanjing Institute of Agricultural Mechanization (CN)
Publication Details
- Journal
- Smart Agricultural Technology
- Published
- 2026-01-14
- DOI
- https://doi.org/10.1016/j.atech.2025.101770
- Citations
- 1
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 31.34