Small unmanned aircraft systems (UAS) are increasingly used for remote sensing applications in precision agriculture due to their ability to collect high-resolution imagery. However, spatial calibration of UAS imagery is often a manual process that requires extensive planning and post-processing, presenting bottlenecks for automating image analysis workflows. This study seeks to address bottlenecks in the photogrammetry workflow that arise from manually tagging ground control points (GCPs) by automating the process. The main objectives included investigating (1) the application of data compression techniques for global navigation satellite system (GNSS) coordinates in generating matrix barcode representations and (2) the recovery of GNSS coordinates from matrix barcodes using a small UAS. GNSS coordinates were compressed using a base-36 encoding schema and encoded into QR code GCPs to reduce the number of alphanumeric characters required. Preliminary in-field testing demonstrated the reliability of recovering QR code GCPs from aerial imagery across various altitudes and exposure settings, with adjustments in exposure compensation mitigating altitude-related recoverability issues. Moreover, results indicated that the processing of aerial imagery into orthomosaic images did not compromise QR code recoverability. Further in-field testing identified QR code GCP background color as a key factor influencing recoverability, with darker colors generally improving recoverability. Statistical analysis validated altitude and background color as significant predictors of QR code GCP recoverability. Future research avenues include incorporating environmental factors such as solar radiation to improve statistical model fit. Overall, QR code GCPs offer a potential approach for automating photogrammetry workflows, reducing both time and labor associated with manual tagging.
Small unmanned aircraft systems (UAS) are a relatively new tool for collecting remote sensing data at dense spatial and temporal resolutions. This study aimed to develop a spectral measurement platform for deployment on a UAS for quantifying and delineating moisture zones within an agricultural landscape. A series of portable spectrometers covering ultraviolet (UV), visible (VIS), and near-infrared (NIR) wavelengths were instrumented using a Raspberry Pi embedded computer that was programmed to interface with the UAS autopilot for autonomous data acquisition. A second set of identical spectrometers were fitted with calibrated irradiance lenses to capture ambient light during data acquisition. Data were collected during the 2017 Great American Eclipse while observing a reflectance target to determine the ability to compensate for ambient light conditions. A calibration routine was developed that scaled raw reflectance data by sensor integration time and ambient light energy. The resulting calibrated reflectance exhibited a consistent spectral profile and average intensity across a wide range of ambient light conditions. Results indicated the potential for mitigating the effect of ambient light when passively measuring reflectance on a portable spectral measurement system. Future work will use multiple reflectance targets to test the ability to classify targets based on spectral signatures under a wide range of ambient light conditions.
Conference Committee Involvement (8)
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping X
13 April 2025 | Orlando, Florida, United States
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IX
22 April 2024 | National Harbor, Maryland, United States
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VIII
1 May 2023 | Orlando, Florida, United States
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VII
4 April 2022 | Orlando, Florida, United States
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VI
12 April 2021 | Online Only, Florida, United States
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V
27 April 2020 | Online Only, California, United States
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV
15 April 2019 | Baltimore, MD, United States
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
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