Field-Based Calibration of Unmanned Aerial Vehicle Thermal Infrared Imagery with Temperature-Controlled References

Abstract

Accurate and reliable calibration methods are required when applying unmanned aerial vehicle (UAV)-based thermal remote sensing in precision agriculture for crop stress monitoring, irrigation planning, and harvesting. The primary objective of this study was to improve the calibration accuracies of UAV-based thermal images using temperature-controlled ground references. Two temperature-controlled ground references were installed in the field to serve as high- and low-temperature references, approximately spanning the expected range of crop surface temperatures during the growing season. Our results showed that the proposed method using temperature-controlled references was able to reduce errors due to ambient conditions from 9.29 to 1.68 °C, when tested with validation panels. There was a significant improvement in crop temperature estimation from the thermal image mosaic, as the error reduced from 14.0 °C in the un-calibrated image to 1.01 °C in the calibrated image. Furthermore, a multiple linear regression model (R2 = 0.78; p-value < 0.001; relative RMSE = 2.42%) was established to quantify soil moisture content based on canopy surface temperature and soil type, using UAV-based thermal image data and soil electrical conductivity (ECa) data as the predictor variables.

Overcoming the Challenges of Thermal Infrared Orthomosaics Using a Swath-Based Approach to Correct for Dynamic Temperature and Wind Effects

“…While the literature provides examples of many precision agricultural applications for UAV-based thermal infrared data, including surface flux retrievals for evapotranspiration studies [51], phenotyping [28], plant water stress assessment [52], and irrigation scheduling [53], they suffer from inconsistencies that would inevitably be introduced by wind effects, especially wind direction, and the existing orthomosaic approaches. Even when applying best-practice field data collections of temperature-controlled ground references [54], inconsistencies from wind effects and the generation of an orthomosaic still exist within the dataset. Hence, our proposed method may have significant implications for thermal data use in precision agriculture, through provision of more consistent and reliable data, as long as other calibration steps, such as those presented by Aragon et al [29] are also followed.…”

A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

“…These sensors detect the radiation related to their wavelengths and generate heat while converting these radiations into grayscale images. Furthermore, they can generate colored images with yellow representing warmer images and blue representing cooler images [163]. Their costs are relatively low and RGB sensors with a few modifications can be converted into thermal sensors.…”

Development of Multiple UAV Collaborative Driving Systems for Improving Field Phenotyping

“…In particular, they are widely used for remote sensing [1,2]. Currently, remote sensing in the agricultural field mainly involves remote exploration using a single UAV system [3][4][5], mapping [6][7][8][9], monitoring [10,11], and DSM [12,13]. The remote sensing process varies depending on the features (characteristics) of crops, the cultivation environment, and the exploration purpose; however, it mostly uses ortho-images obtained using red-green-blue (RGB) cameras and multi-spectral cameras.…”