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

Abstract

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.

Leaf segmentation in plant phenotyping: a collation study

“…This is due to the variability in shape, pose, and appearance of leaves, but also due to lack of clearly discernible boundaries among overlapping leaves with typical imaging conditions where a top-view fixed camera is used. Several authors have dealt with the segmentation of a live plant from background to measure growth using unsupervised [17,25] and semi-supervised methods [36], but not of individual leaves. The use of color in combination with depth images or multiple images for supervised or unsupervised plant segmentation is also common practice [4,10,29,46,49,51,52,57].…”

“…Several specialized algorithms have been developed to measure whole-plant properties and particularly plant size [6,17,25,27,31,40,55,61]. Nondestructive measurement of a plant's projected leaf area (PLA), i.e., the counting of plant pixels from top-view images, is considered a good approximation of plant size for rosette plants and is currently used.…”

3D sorghum reconstructions from depth images identify QTL regulating shoot architecture

“…Commercial platforms, including the Scanalyzer series from Lemnatec (http:// www.lemnatec.com/products/; accessed February 2016) and the Traitmill platform from CropDesign (http:// www.cropdesign.com/general.php; accessed February 2016), have gained adoption in the research community and have promoted the development of additional software (beyond that which the respective companies provide) to analyze the images produced by the platform (Reuzeau, 2007;Hartmann et al, 2011;Fahlgren et al, 2015a). A variety of noncommercial platforms and methods developed by the research community also exist and have been demonstrated to perform well (White et al, 2012;Fiorani and Schurr, 2013;Sirault et al, 2013;Pound et al, 2014).…”

Nordic research infrastructures for plant phenotyping

“…Several open source or freeware software are available to analyze the images for measuring various morphological traits. Examples of software that can be installed on local computers are HTPheno (Hartmann et al 2011), Integrated Analysis Platform (IAP) (Klukas et al 2014), ImageHarvest (Knecht et al 2016), Plant Computer Vision (PlantCV) (Fahlgren et al 2015) and Easy Leaf Area (Easlon and Bloom 2014). These software vary in their user friendliness, available features, computational resource requirements and the traits measured.…”