HTPheno: An image analysis pipeline for high-throughput plant phenotyping

By Anton Hartmann, Tobias Czauderna, R. Hoffmann, Nils Stein, Falk Schreiber. 2011

298 citations recorded by the source corpus.

BackgroundIn the last few years high-throughput analysis methods have become state-of-the-art in the life sciences. One of the latest developments is automated greenhouse systems for high-throughput plant phenotyping. Such systems allow the non-destructive screening of plants over a period of time by means of image acquisition techniques. During such screening different images of each plant are recorded and must be analysed by applying sophisticated image analysis algorithms.ResultsThis paper presents an image analysis pipeline (HTPheno) for high-throughput plant phenotyping. HTPheno is implemented as a plugin for ImageJ, an open source image processing software. It provides the possibility to analyse colour images of plants which are taken in two different views (top view and side view) during a screening. Within the analysis different phenotypical parameters for each plant such as height, width and projected shoot area of the plants are calculated for the duration of the screening. HTPheno is applied to analyse two barley cultivars.ConclusionsHTPheno, an open source image analysis pipeline, supplies a flexible and adaptable ImageJ plugin which can be used for automated image analysis in high-throughput plant phenotyping and therefore to derive new biological insights, such as determination of fitness.

Subfields: Biophysics, Computer Vision and Pattern Recognition, Plant Science, Computational Biology & Bioinformatics

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Citations in the corpus

Finely-grained annotated datasets for image-based plant phenotyping (2016, methodology): “…al., 2009; Nagel et al., 2012) together with customized image processing pipelines to analyze aspects of the acquired image data (Au-gustin et al., 2015; De Vylder et al., 2012; Hartmann et al., 2011; van der Heijden et al., 2012; Walter et al., 2007; Yin et al., 2014; M¨uller-Linow et al., 2015).”
Marker-Assisted Plant Breeding: Principles and Practices (2015, methodology): “The images are analyzed by image-processing software dedicated to the specific platforms (Hartmann et al. 2011).”
An interactive tool for semi-automated leaf annotation (2015, methodology): “In plant applications most annotation tools are limited towards annotating whole plants (hence, not individual leaves) [9, 16, 24], leaves in isolation (e.”
Plant phenotyping: from bean weighing to image analysis (2015, methodology): “Such methods have been successfully applied in the laboratory to assess the performance of Arabidopsis [68], tobacco [69] or cereal grain crops [11,12].”
Dissecting the Phenotypic Components of Crop Plant Growth and Drought Responses Based on High-Throughput Image Analysis[W][OPEN] (2014, methodology): “The number of traits, including image-based features and model-derived parameters, extracted from our pipeline greatly exceeds existing pipelines (Wang et al., 2009; Hartmann et al., 2011; De Vylder et al., 2012; Green et al., 2012; Paproki et al., 2012; Zhang et al., 2012; Camargo et al., 2014).”
Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks (2017, methodology): “Many image analysis tools have been released by the scientiﬁc community for the purpose of performing image-based high-throughput plant phenotyping (Hartmann et al., 2011; Fahlgren et al., 2015a; Rahaman et al., 2015; Knecht et al., 2016).”
Learning to Count Leaves in Rosette Plants (2015, background): “High throughput plant phenotyping allows large-scale plant analysis [13, 17, 39], in the hope of reducing the bottleneck”
A Review of Imaging Techniques for Plant Phenotyping (2014, background): “These platforms are specifically designed for research and large scale phenotyping for a limited range of species, encompassing small rosette plants such as Arabidopsis [15,27] and the primary cereal crops [13,29,198].”
REVIEW: PART OF A HIGHLIGHT ON BREEDING STRATEGIES FOR FORAGE AND GRASS IMPROVEMENT Advanced phenotyping offers opportunities for improved breeding of forage and turf species (2012, methodology): “Also, the benefit of such methods to detect genotypic differences to drought susceptibility or other environmental stresses has been shown in oilseed rape (Jansen et al., 2009) and cereals (Rajendran et al., 2009; Hartmann et al., 2011).”
Image-based plant phenotyping with incremental learning and active contours (2014, methodology): “On the other hand, some approaches assume static cameras with a fixed field of view (De Vylder et al., 2012; Hartmann et al., 2011; Kokorian et al., 2010; Tsaftaris and Noutsos, 2009) and are largely used in growth-chamber experiments of small plant models such as Arabidopsis.”
segmentation in plant phenotyping: a collation study. Machine Vision and Applications (2017, background): “Several specialized algorithms have been developed to measure whole-plant properties and particularly plant size [6,17,25,27,31,40, 55,61].”
Crop Phenomics and High-throughput Phenotyping: Past Decades, Current Challenges and Future Perspectives. (2020, unknown): “To alleviate this problem, the Leibniz Institute of Plant Genetics and Crop Plant Research developed HTPheno, a plugin for ImageJ, and other open-source image-processing software to extract standard traits from color images, such as plant height, width, and projected area (Hartmann et al., 2011).”
Morphological and physiological traits for higher biomass production in perennial rhizomatous grasses grown on marginal land (2015, methodology): “This involves applying sophisticated image analysis algorithms (Hartmann et al., 2011) to extract several phenotypic parameters related to growth, yield and stress tolerance and as a consequence reduces the tedious and time consuming manual analysis of large numbers of phenotypes.”