Optimizing UAV-based uncooled thermal cameras in field conditions for precision agriculture

Quanxing Wan , Magdalena Smigaj , Benjamin Brede , Lammert Kooistra
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Abstract

Unoccupied aerial vehicles (UAVs) equipped with thermal cameras show great promise for precision agriculture, but challenges persist in analyzing land surface temperature (LST). This study explores the influence of ambient environmental conditions and intrinsic characteristics of uncooled thermal cameras on the accuracy of temperature measurements obtained through UAV-based thermal cameras. The research utilized DJI Matrice 210 quad-rotor UAVs equipped with FLIR Tau 2 and WIRIS 2nd Gen thermal cameras. The experimental design involved strategically selected temperature reference materials of diverse compositions. UAV flights were conducted at varying altitudes, capturing thermal images correlated with ground-based thermocouple measurements. Results indicate that increasing flight altitude resulted in underestimated temperatures measured by UAVs for objects with higher kinematic temperatures, while objects with lower temperatures displayed higher measurements. The study integrates multiple environmental metrics, illustrating the complex influence of air temperature, humidity, net radiation, and wind speed on temperature measurements, with variations observed between FLIR Tau 2 and WIRIS 2nd Gen camera models. Linear regression models highlight the diverse impact of these metrics on UAV-based temperature observations. Furthermore, an analysis of uncooled thermal sensor characteristics reveals a correlation between UAV-measured temperatures and the focal plane array (FPA) temperature, emphasizing the importance of considering intrinsic sensor dynamics. These findings provide valuable insights for enhancing the reliability of UAV-based thermal measurements in agricultural and environmental monitoring. The research underscores the necessity for a comprehensive understanding of both ambient conditions and camera-model-specific dynamics to optimize thermal imaging accuracy for precision agriculture applications. Accordingly, the recommended procedures have been described to reduce the effect of identified sources of influence.
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优化田间条件下基于无人机的非制冷红外热像仪,促进精准农业发展
配备热像仪的无人飞行器(UAV)在精准农业方面大有可为,但在分析地表温度(LST)方面仍存在挑战。本研究探讨了周围环境条件和非制冷红外热像仪固有特性对无人飞行器红外热像仪温度测量精度的影响。研究使用了配备 FLIR Tau 2 和 WIRIS 第二代红外热像仪的大疆 Matrice 210 四旋翼无人机。实验设计包括战略性地选择不同成分的温度参考材料。无人机在不同高度飞行,捕捉与地面热电偶测量结果相关的热图像。结果表明,飞行高度的增加导致无人机对运动温度较高的物体所测得的温度被低估,而温度较低的物体则显示出较高的测量值。该研究整合了多个环境指标,说明了空气温度、湿度、净辐射和风速对温度测量的复杂影响,并观察到 FLIR Tau 2 和 WIRIS 第二代相机型号之间的差异。线性回归模型凸显了这些指标对无人机温度观测的不同影响。此外,对非制冷热传感器特性的分析表明,无人机测量的温度与焦平面阵列(FPA)温度之间存在相关性,强调了考虑传感器内在动态的重要性。这些发现为提高无人机热测量在农业和环境监测中的可靠性提供了宝贵的见解。研究强调,必须全面了解环境条件和相机模型的特定动态,以优化精准农业应用中的热成像精度。因此,已对建议的程序进行了说明,以减少已确定的影响源的影响。
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来源期刊
International journal of applied earth observation and geoinformation : ITC journal
International journal of applied earth observation and geoinformation : ITC journal Global and Planetary Change, Management, Monitoring, Policy and Law, Earth-Surface Processes, Computers in Earth Sciences
CiteScore
12.00
自引率
0.00%
发文量
0
审稿时长
77 days
期刊介绍: The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.
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