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The plant breeding community has increasingly adopted remote sensing platforms like unmanned aerial vehicles (UAVs) to collect crop phenotype data. These platforms captured high-resolution multi-spectral (MS) image data during extensive field trials, enabling concurrent evaluation of thousands of plots with diverse seed varieties and management practices. However, breeders still relied on manual and intricate data extraction, processing, and analysis of high-resolution imagery for drawing results. A significant challenge was identifying plot locations within MS imagery, delineating plot boundaries, and providing spatial references to each plot. Therefore, the study was conducted to develop an automated methodology for creating multiple polygon shape files with unique identifiers for overlaying drone imagery data with centimeter-level accuracy for obtaining zonal statistics of each phenotype plot. The goal was to develop a pipeline without assuming field uniformity, plot spacing, size, or quantity, minimizing the need for manual adjustments. The proposed method used a highaccuracy planter-logged Real-Time Kinematic Global Positioning System (RTK-GPS) and georeferenced MS-UAV imagery. This process automatically generated plot boundaries by converting RTK-GPS points to polygons representing each planted plot. The resulting pipeline automatically produced maps of Vegetative Indices (VI), multi-polygon shape files, and CSV files of plot boundaries for external software and downstream analysis. Notably, the polygon shape file consistently aligned with plot boundaries within the image and even across temporal data sets with the least manual adjustments. This approach provided an efficient, adaptable, and replicable automated solution, minimizing time, labor, and user involvement while facilitating zonal statistics extraction of each phenotype plot from MS imagery.
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