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ARS Home » Plains Area » Fargo, North Dakota » Edward T. Schafer Agricultural Research Center » Sugarbeet Research » Research » Publications at this Location » Publication #417484
Research Project: Improving Sugarbeet Productivity and Sustainability through Genetic, Genomic, Physiological, and Phytopathological Approaches

Location: Sugarbeet Research

Title: DIY image analytics for UAS-based plant phenotyping

Author
item Kim, James

Submitted to: North American Plant Phenotyping Network Meeting
Publication Type: Proceedings
Publication Acceptance Date: 12/15/2023
Publication Date: 12/15/2023
Citation: Kim, J.Y. 2023. DIY image analytics for UAS-based plant phenotyping. North American Plant Phenotyping Network Meeting. http://doi.org/10.22541/essoar.169868664.47571324/v1.
DOI: https://doi.org/10.22541/essoar.169868664.47571324/v1

Interpretive Summary:

Technical Abstract: Technical Abstract: UAS-based image analytics has been deployed to expedite the plant phenotyping and replace laborious manual notetaking but is limited for global validation in all field conditions. Plot-level metrics is essential for plant phenotyping on many plots and extracted by defining a region of interest (ROI) of the field boundary and processing sub-ROIs aligned with rows and columns of the total number of plots, called gridding. Gridding is offered by commercial software but is limited to upright rectangular fields. When UAS tile images are stitched, an orthomosaic image is georeferenced to make the image top to north, whereas the field orientation is often off the north. Due to the misaligned orientation, the gridding process requires a preprocess of image rotation to align the grid onto the field boundary, which creates resampling errors and takes laborious multiple adjustments to precisely align sub-ROIs with plots across the field. To address this issue, an open-source software was developed to generalize the gridding method and provide a quick extraction of plot-level metrics without the image rotation. Adaptive gridding algorithm is to rotate the grid by applying geometry of a rectangle in a circle that keeps right angles. Metrics of the rotated ROI is calculated by geofencing pixels in the ROI for segmentation, filtering, masking, and clustering. The open-source software with adaptive gridding allows the end-users to process their UAS images for high throughput phenotyping in an effective manner without understanding details of image processing.