数字农科院2.0

UAV remote sensing-driven precision variable management in cotton: technological framework, applications, and research outlook

文献类型： 外文期刊

作者： Lechun Zhang;Yingkuan Wang;Xinyu Xue;Wenjiang Huang;Tianye Yang;Hang Zhu;Yubin Lan

作者机构：

关键词： Cotton;Precision agriculture;Remote sensing;UAV;Variable-rate application

期刊名称： Computers and Electronics in Agriculture

ISSN： 0168-1699

年卷期： 2026 年 243 卷

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： This review focuses on unmanned aerial vehicle (UAV) remote sensing–driven precision variable management in cotton fields. It systematically examines the key processes and engineering constraints from observable indicators to executable prescriptions within the closed loop of sensing, prescription, execution, and quality feedback. First, the review back-derives observable quantities and payload configurations from task requirements. It compares RGB, multispectral, hyperspectral, thermal infrared, and LiDAR sensing modes in terms of coverage, resolution, and temporal window, and highlights the value of multisource information in reducing prescription uncertainty under strict geometric co-registration. Second, it summarizes the scale transformation from ground sample distance (GSD) to control units, proposing that denoising and connectivity purification should be completed within the computational domain before aggregation to achieve an effective prescription resolution. Spatial foresight compensation is further introduced to mitigate end-to-end latency, preventing high-frequency toggling and striping coverage. Furthermore, the review summarizes three prescription modeling paradigms (index, mechanism, and learning) and clarifies their complementarity, strengths, limitations, and suitable use cases. On the execution side, the review analyzes how prescription encoding interacts with onboard decoding constraints. It covers RTK/GNSS positioning, PWM frequency and duty-cycle stability ranges, pressure and flow dynamics, droplet spectrum categories, and operational meteorological windows, and provides practical recommendations on the minimum executable patch size and dual-threshold hysteresis. Overall, UAVs can reliably support monitoring, decision-making, and execution in cotton fields characterized by short operational windows and fine spatial heterogeneity. Establishing prescription-level metadata and standardized quality feedback mechanisms is essential to enable cross-field, cross-season generalization and large-scale implementation.

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