We address fast image stitching for large image collections while being robust to drift due to chaining transformations and minimal overlap between images. We focus on scientific applications where ground truth accuracy is far more important than visual appearance or projection error, which can be misleading. For common largescale image stitching use cases, transformations between images are often restricted to similarity or translation. When homography is used in these cases, the odds of being trapped in a poor local minimum and producing unnatural results increases. Thus, for transformations up to affine, we cast stitching as minimizing reprojection error globally using linear least squares with a few, simple constraints. For homography, we observe that the global affine solution provides better initialization for bundle adjustment compared to an alternative that initializes with a homography-based scaffolding, and at lower computational cost. We evaluate our methods on a very large translation dataset with limited overlap, as well as four drone datasets. We show that our approach is better compared to alternative methods such as MGRAPH in terms of computational cost, scaling to large numbers of images, and robustness to drift. We also contribute ground truth datasets for this endeavor.

Small unoccupied aircraft systems (sUAS) are becoming popular for mapping applications in agriculture, and photogrammetry software is available for developing orthorectified imagery and three-dimensional surface models. Ground control points (GCPs), which are objects or locations with known geographic coordinates, may be required for accurate image georeferencing. However, few studies have compared global position equipment among sUAS or investigated the effects of GCP number or arrangement on georeferencing accuracy. The objectives of this study were to evaluate numbers and configurations of GCPs for georeferencing sUAS-acquired images and determine the GCP requirements for sUAS with and without real-time kinematic (RTK) global positioning equipment. The effects of varying numbers and configurations of GCPs were investigated on both a 0.40-ha area the size of a typical plant breeding trial and a 64.7-ha area (i.e., a U.S. quarter section) the size of a typical agricultural production field. Results demonstrated that four GCPs placed at the corners of the breeding-scale field resulted in two-dimensional (2D) error of ±3 cm in the absence of RTK, with minimal improvements when including more GCPs. The orthomosaics from the RTK-equipped sUAS demonstrated improved 2D accuracy even without the use of GCPs, with a maximum mean error of 0.08 m. Four GCPs were found to be sufficient to reduce altitudinal (Z) error, with maximum mean error of only 0.05 and 1.98 m for the RTK and non-RTK flights, respectively, for the production-scale field. Thus, using four GCPs, RTK-equipped sUAS, or a combination will result in improved georeferencing for photogrammetry products.

Method details are available in the manuscript.<br>

As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to (i) improve data processing efficiency; (ii) provide an extensible, reproducible computing framework; and (iii) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area).

No abstract