Introduction

Detecting and monitoring crop stress is crucial for ensuring sufficient and sustainable crop production. Recent advancements in unoccupied aerial vehicle (UAV) technology provide a promising approach to map key crop traits indicative of stress. While using single optical sensors mounted on UAVs could be sufficient to monitor crop status in a general sense, implementing multiple sensors that cover various spectral optical domains allow for a more precise characterization of the interactions between crops and biotic or abiotic stressors. Given the novelty of synergistic sensor technology for crop stress detection, standardized procedures outlining their optimal use are currently lacking.

Materials and methods

This study explores the key aspects of acquiring high-quality multi-sensor data, including the importance of mission planning, sensor characteristics, and ancillary data. It also details essential data pre-processing steps like atmospheric correction and highlights best practices for data fusion and quality control.

Results

Successful multi-sensor data acquisition depends on optimal timing, appropriate sensor calibration, and the use of ancillary data such as ground control points and weather station information. When fusing different sensor data it should be conducted at the level of physical units, with quality flags used to exclude unstable or biased measurements. The paper highlights the importance of using checklists, considering illumination conditions and conducting test flights for the detection of potential pitfalls.

Conclusion

Multi-sensor campaigns require careful planning not to jeopardise the success of the campaigns. This paper provides practical information on how to combine different UAV-mounted optical sensors and discuss the proven scientific practices for image data acquisition and post-processing in the context of crop stress monitoring.

中文翻译：

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无人机作物胁迫检测：利用传感器协同作用的最佳实践和经验教训

 介绍

检测和监测作物胁迫对于确保充足和可持续的作物生产至关重要。无人飞行器（UAV）技术的最新进展为绘制指示胁迫的关键作物性状提供了一种有前景的方法。虽然使用安装在无人机上的单个光学传感器足以监测一般意义上的作物状态，但采用覆盖各种光谱光学域的多个传感器可以更精确地表征作物与生物或非生物应激源之间的相互作用。鉴于用于作物胁迫检测的协同传感器技术的新颖性，目前缺乏概述其最佳用途的标准化程序。

 材料和方法

本研究探讨了获取高质量多传感器数据的关键方面，包括任务规划、传感器特性和辅助数据的重要性。它还详细介绍了大气校正等基本数据预处理步骤，并重点介绍了数据融合和质量控制的最佳实践。

 结果

成功的多传感器数据采集取决于最佳时机、适当的传感器校准以及地面控制点和气象站信息等辅助数据的使用。当融合不同的传感器数据时，应在物理单元级别进行，并使用质量标志来排除不稳定或有偏差的测量结果。该论文强调了使用检查表、考虑照明条件以及进行试飞来检测潜在陷阱的重要性。

 结论

多传感器活动需要仔细规划，以免危及活动的成功。本文提供了有关如何组合不同无人机安装的光学传感器的实用信息，并讨论了作物胁迫监测背景下图像数据采集和后处理的经过验证的科学实践。