Kibon Ku, Thai Thanh Tuan, Jinhyun Ahn · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.26719 · cited by 0 works (OpenAlex) · open-access PDF
Die Zeit-Serie-Multispektralbildgebung wird weit verbreitet verwendet, um den Turfgrass-Zustand zu beurteilen; jedoch bleibt die Extraktion von Plot-Level-Vegetation Index (VI) -Werten arbeitsintensiv, wenn die Positionen der Kamera zwischen Akquisitionen variieren und die automatisierte Plot-Tracking ist unzuverlässig. Wir haben ein Open-Source-Software-Tool entwickelt, das (i) eine grafische Benutzeroberfläche (GUI) für die interaktive Platzierung von Orientierungs-anpassbaren Interessengebieten (ROIs) mit N$-Wege-Unterteilung zur Übereinstimmung mit Plotgeometrie umfasst. und (ii) ein Batch-Verarbeitungs-Skript, das Plot-Level-M
Machine translation (Meta M2M100); the English original follows.
Time-series multispectral imaging is widely used to assess turfgrass condition; however, extracting plot-level vegetation index (VI) values remains labor-intensive when camera positions vary between acquisitions and automated plot tracking is unreliable. We developed an open-source software tool comprising (i) a graphical user interface (GUI) for the interactive placement of orientation-adjustable regions of interest (ROIs) with $N$-way subdivision to match plot geometry and (ii) a batch-processing script that computes plot-level mean VI values from single-band floating-point rasters and exports measurements and ROI coordinates in JSON format for reuse. Saved coordinates can be reapplied to measure NDVI, NDRE, and SAVI from identical pixel regions within a single time point without re-delineating ROIs. In a demonstration using rail-mounted multispectral turfgrass imagery (18 images), plot-level mean values were extracted in a single session, with coordinates reused directly for NDRE and SAVI rasters. The system reads non-georeferenced pixel-coordinate rasters natively, does not require GIS expertise, and supports reproducible VI quantification where static sensor deployment is impractical.
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Topics: Computer science, Multispectral image, Computer vision, Pixel, Remote sensing, Artificial intelligence
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