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Calibrating UAV-Based Thermal Remote-Sensing Images of Crops with Temperature Controlled References

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2019 ASABE Annual International Meeting  1900662.(doi:10.13031/aim.201900662)
Authors:   Xiongzhe Han, Alex Thomasson, Jeffrey Siegfried, Rahul Raman, Nithya Rajan, Haly Neely
Keywords:   Unmanned aerial vehicle, Thermal remote sensing, Thermal calibration tiles, Crop temperature, Temperature controlled references

Abstract. Thermal remote sensing for the measurement of soil and crop surface temperatures has potential for various applications including the monitoring of crop stresses (like diseases and lack of soil moisture) and the planning of irrigation and harvesting. The use of unmanned aerial vehicles (UAVs) to acquire highly accurate thermal image data requires integration with thermal references on the ground. The primary objective of this paper was to demonstrate that it is possible to combine thermal remote sensing with UAV and temperature controlled ground references for calibrated crop temperature measurements. The references used for calibration of thermal images were two 61 cm square aluminum panels – one equipped with an integrated heating controller, thermal sensors, and thermoelectric modules to serve as a high-temperature reference, and the other equipped with an integrated cooling controller, thermal sensors, and coolers to serve as a low-temperature reference. To demonstrate the feasibility of using the calibration references in thermal images, three groups with three 61 cm square color tiles (light gray, medium gray, and dark gray) were distributed on the ground at different tilted angles (0 degrees, 25 degrees, and 50 degrees) to consider the effect that variations in crop temperature have on stem or leaf bending. Correlations between UAV-based thermal image estimates and ground truth were strong (R=0.98) on both un-calibration and calibration procedures. It is clear that a thermal calibration method based on ground temperature controlled references to improve UAV-based wheat temperature estimates was applied to a lower error by about 4%.

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