Abstract
Background. Efficient analysis of large image data produced in greenhouse phenotyping experiments is often challenged by a large variability of optical plant and background appearance which requires advanced classification model methods and reliable ground truth data for their training. In the absence of appropriate computational tools, generation of ground truth data has to be performed manually, which represents a time-consuming task. Methods. Here, we present a efficient GUI-based software solution which reduces the task of plant image segmentation to manual annotation of a small number of image regions automatically pre-segmented using k-means clustering of Eigen-colors (kmSeg). Results. Our experimental results show that in contrast to other supervised clustering techniques k-means enables a computationally efficient pre-segmentation of large plant images in their original resolution. Thereby, the binary segmentation of plant images in fore- and background regions is performed within a few minutes with the average accuracy of 96–99% validated by a direct comparison with ground truth data. Conclusions. Primarily developed for efficient ground truth segmentation and phenotyping of greenhouse-grown plants, the kmSeg tool can be applied for efficient labeling and quantitative analysis of arbitrary images exhibiting distinctive differences between colors of fore- and background structures.
Highlights
IntroductionPublished: 4 November 2021With the introduction of high-throughput plant phenotyping facilities, efficient analysis of large multimodal image data turned into focus of quantitative plant research [1].Typical goals of high-throughput plant image analysis include detection, counting or pixel-wise segmentation of targeted plant structures (e.g., whole shoots, fruits, spikes, etc.)in field or greenhouse environments followed by their quantitative characterization in terms of morphological, developmental and/or functional traits
Published: 4 November 2021With the introduction of high-throughput plant phenotyping facilities, efficient analysis of large multimodal image data turned into focus of quantitative plant research [1].Typical goals of high-throughput plant image analysis include detection, counting or pixel-wise segmentation of targeted plant structuresin field or greenhouse environments followed by their quantitative characterization in terms of morphological, developmental and/or functional traits
The k-means clustering of Eigen-colors (kmSeg) tool was primarily developed for ground truth segmentation of visible light (VIS) and fluorescence (FLU) images of maize, wheat and arabidopsis shoots acquired from greenhouse phenotyping experiments using LemnaTec-Scanalyzer3D high-throughput phenotyping platforms (LemnaTec GmbH, Aachen, Germany)
Summary
Published: 4 November 2021With the introduction of high-throughput plant phenotyping facilities, efficient analysis of large multimodal image data turned into focus of quantitative plant research [1].Typical goals of high-throughput plant image analysis include detection, counting or pixel-wise segmentation of targeted plant structures (e.g., whole shoots, fruits, spikes, etc.)in field or greenhouse environments followed by their quantitative characterization in terms of morphological, developmental and/or functional traits. With the introduction of high-throughput plant phenotyping facilities, efficient analysis of large multimodal image data turned into focus of quantitative plant research [1]. Typical goals of high-throughput plant image analysis include detection, counting or pixel-wise segmentation of targeted plant structures (e.g., whole shoots, fruits, spikes, etc.). Due to a number of natural and technical factors, segmentation of plant structures from background image regions represents a challenging task. Efficient analysis of large image data produced in greenhouse phenotyping experiments is often challenged by a large variability of optical plant and background appearance which requires advanced classification model methods and reliable ground truth data for their training. Our experimental results show that in contrast to other supervised clustering techniques k-means enables a computationally efficient pre-segmentation of large plant images in their original resolution. Thereby, the binary segmentation of plant images in fore- and background regions is performed within a few minutes with the average accuracy of
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