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A Flexible ROI Analysis Tool for Spatially Consistent Vegetation Index Extraction from Turfgrass Imagery

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

Portuguese

A imagem multispectral da série do tempo é amplamente utilizada para avaliar a condição da turfa; no entanto, a extração de valores do índice de vegetação (VI) de nível de enredo permanece intensiva quando as posições da câmara variam entre aquisições e o rastreamento automatizado da enredo é pouco confiável. Desenvolvemos uma ferramenta de software de código aberto que inclui (i) uma interface gráfica do usuário (GUI) para a colocação interativa de regiões de interesse ajustáveis de orientação (ROIs) com subdivisão de $N$ para a geometria da enredo e (ii) um script de processamento de lote que calcula os valores médios do enredo VI de raster de ponto flutuante de banda única e exporta medições e coordenadas de ROI no formato JSON para

Machine translation (Meta M2M100); the English original follows.

Abstract

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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