Enhancing Spaceflight Imaging Data Using Simple Online Automated Plant Phenomics (SOAPP)

Abstract

Abstract Plant phenomics is an emerging discipline that uses image analysis to extract quantitative phenotypic data to understand plant growth and development. However, phenomics tools often require a precise format of image acquisition that is set during software development or that is part of a proprietary software environment. To remove these barriers and align with NASA's Transform to Open Science initiative, we have created a web-based application to measure plant aerial phenotypes called Simple Online Automated Plant Phenomics (SOAPP). SOAPP uses two open-source Python packages, PlantCV and OpenCV, and is available either online as a web application or can be run locally from a Docker image. Users simply upload their images, select sample-specific color spaces, and specify regions of interest. Foliage size, shape characteristics, and color values are then automatically extracted. SOAPP has been successfully used to characterize plant growth in hydroponic systems, pots, and Petri plates. ArUco machine-recognizable tags further allow automated scale-finding, image plane correction, and color standardization and correction. These adjustments for variations in the distance and axis at which the images were taken greatly enhance the quantitative accuracy of data extracted from hand-held crew photography, enhancing science return from both current and future spaceflight settings.

Plant phenotyping relevance

植物画像から葉面積・形状・色を抽出するウェブ型フェノタイピング手法SOAPPの開発と精度向上が中心であり、明確な方法開発・プラットフォーム研究である。

Code and data availability

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