High-accuracy Infrared Thermography of Cotton Canopy Temperature by Unmanned Aerial Systems (UAS): Evaluating In-Season Prediction of Yield
SMART AGRICULTURAL TECHNOLOGY(2024)
摘要
Canopy temperature in cotton (Gossypium hirsutum) and other crops is related to crop and soil water status. Multiple approaches have been used to measure canopy temperature, depending on the application of the data and available technology. Recent technological advances have made it possible to map canopy temperature using thermal cameras onboard unmanned aerial systems (UAS) at fine spatiotemporal resolution. Using a highly accurate UAS-mounted infrared camera, the objective of this study was to determine relationships of in-season cotton canopy temperature with seed cotton yield and soil moisture. This was accomplished in a two-year field study (2019 and 2020) with three irrigation (0, 40, and 80% ET replacement) and eight cotton variety treatments. Four in-season UAS flights were made per year. Using the approach described herein, it was possible to accurately exclude soil background information in thermal infrared orthomosaics using a simple threshold method without the need for ancillary imagery. This allowed accurate determination of average canopy temperatures across plots. Differences in canopy temperature and seed cotton yields across irrigation levels were largely aligned and, when comparing cotton varieties, there was evidence that canopy temperature identified subtle varietal differences that were not reflected in varietal yield differences. Overall, there were linear relationships between canopy temperature and seed cotton yield that ranged from weak to strong (R2 = 0.39 – 0.72, RMSE = 249.7 – 299.2 kg ha−1), depending on growing conditions. Canopy temperature was generally correlated with soil volumetric water content (VWC), but the relationship was not consistently strong and depended on moisture sensor depth.
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关键词
Cotton,Crop phenotyping,High-throughput phenotyping,Infrared temperature,Plant phenotyping,Remote sensing,Thermal camera,Unmanned aerial vehicle
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