Maize Canopy Temperature Extracted From UAV Thermal and RGB Imagery and Its Application in Water Stress Monitoring

Abstract

To identify drought-tolerant crop cultivars or achieve a balance between water use and yield, accurate measurements of crop water stress are needed. In this study, the canopy temperature (Tc) of maize at the late vegetative stage was extracted from high-resolution red–green–blue (RGB, 1.25 cm) and thermal (7.8 cm) images taken by an unmanned aerial vehicle (UAV). To reduce the number of parameters for crop water stress monitoring, four simple methods that require only Tc were identified: Tc, degrees above non-stress, standard deviation of Tc, and variation coefficient of Tc. The ground-truth temperatures obtained using a handheld infrared thermometer were used to calibrate the temperature obtained from the UAV thermal images and to evaluate the Tc extraction results. Measured leaf stomatal conductance values were used to evaluate the performance of the four Tc-based crop water stress indicators. The results showed a strong correlation between ground-truth Tc and Tc extracted by the red–green ratio index (RGRI)-Otsu method proposed in this study, with a coefficient of determination of 0.94 (n = 15) and root mean square error value of 0.7°C. The RGRI-Otsu method was most accurate for estimating temperatures around 32.9°C, but the magnitude of residuals increased above and below this value. This phenomenon may be attributable to changes in canopy cover (leaf curling) under water stress, resulting in changes in the proportion of exposed sunlit soil in UAV thermal orthophotographs. Therefore, to improve the accuracy of maize canopy detection and extraction, optimal methods and better strategies for eliminating mixed pixels are needed. This study demonstrates the potential of using high-resolution UAV RGB images to supplement UAV thermal images for the accurate extraction of maize Tc.

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

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Genebank Phenomics: A Strategic Approach to Enhance Value and Utilization of Crop Germplasm

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Breeding for Abiotic Stress Adaptation in Chickpea (Cicer arietinum L.): A Comprehensive Review

“…phenology, early vigor, crop growth status, water content, biomass, and yield potential [117,118]. A plethora of optical devices such as passive (FieldSpec spectroradiometer; [119]) and active sensors (Crop Circle; [120]); red, green and blue (RGB) [121,122], multispectral [123], hyperspectral camera [124] and thermal camera [125] are available. The Light Detection and Ranging (LiDAR; [126]) and LeasyScan PlantEye ® [127] scanning systems emit laser pulses that capture the timing and intensity of the pulse bouncing back from the crop canopy to reconstruct 3D…”