Triangle, Otsu, mean, and Gaussian auto-thresholding functions were added to PlantCV to further improve object detection when image light sources are variable. PlantCV contributors are asked to follow the PEP8 Python style guide (https://www.python.org/dev/peps/pep-0008/).

In addition to the median_blur function included in PlantCV v1.0, we have added a Gaussian blur smoothing function to reduce image noise and detail.

In command-line mode, an entire pipeline script must be executed, even if only a single step is being evaluated. The documentation was updated to cover all functions in the PlantCV library, tutorials on building pipelines and using specialized tools (e.g., multi-plant analysis and machine learning tools), a frequently asked questions section, and several guides such as installation, Jupyter notebooks, and instructions for contributors. The landmark functions in PlantCV output untransformed point values that can either be directly input into morphometric programs in R (shapes (Dryden & Mardia, 2016) or morpho (Schlager, 2017)) or uniformly rescaled to a 0-1 coordinate system using the PlantCV scale_features function. You can add specific subject areas through your profile settings. The resulting images of individual plants can be processed by standard PlantCV methods.

Additional details about the imaging set-up are provided in a companion paper (Tovar et al., 2017). plant wps phenotyping modules overview systems automated Type I landmarks provide the strongest support for homology because they are defined by underlying biological features, but it is problematic to assign Type I landmarks a priori when analyzing high-throughput plant imagery. An alternative approach to using a fixed, global threshold for image segmentation is to use an auto-thresholding technique that either automatically selects an optimal global threshold value or introduces a variable threshold for different regions in an image.

Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. The watershed segmentation function can be used to segment and estimate the number of objects in an image.

Utilizing a rectangular neighborhood around a center pixel, median_blur replaces each pixel in the neighborhood with the median value. Suggestions on how to approach image analysis with PlantCV, in addition to specific tutorials, are available through online documentation (http://plantcv.readthedocs.io/en/latest/analysis_approach/). Suxing Liu and Argelia Lorence conceived and designed the experiments, contributed reagents/materials/analysis tools, wrote the paper, reviewed drafts of the paper. Here we define high-throughput as thousands or hundreds of thousands of images per dataset. An example VIS/NIR dual pipeline to follow can be accessed online (http://plantcv.readthedocs.io/en/latest/vis_nir_tutorial/). 4A). The pull request mechanism is essential to protect against merge conflicts, which are sections of code that have been edited by multiple users in potentially incompatible ways. Eric Platon contributed to the research described while working as a founder and employee of Cosmos X. Tony Sax contributed to the research described while a full-time student at the Missouri University of Science and Technology. (A) Probability density functions (PDFs) from the plantcv-train.py script that show hue, saturation, and value color channel distributions of four classes estimated from training data. Malia A. Gehan, Noah Fahlgren and Max J. Feldman conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.

First, GitHub was used as a platform to organize the community by integrating version control, code distribution, documentation, issue tracking, and communication between users and contributors (Perez-Riverol et al., 2016). Additionally, the use of the Python package Matplotlib (Hunter, 2007) in PlantCV v1.0 limited the number of usable processors to 1012. It is assumed that the pot position changes consistently between VIS and NIR image datasets. In a survey of corresponding authors of plant image analysis tools by Lobet, 60% either said the tool was no longer being maintained or did not respond (Lobet, 2017). "Following" is like subscribing to any updates related to a publication. Images of wheat (Triticum aestivum L.) infected with wheat stem rust (Puccinia graminis f. sp. Jupyter compatibility allows users to immediately visualize output and to iteratively rerun single steps in a multi-step PlantCV pipeline, which makes parameters like thresholds or regions of interest much easier to adjust. Marker peaks calculated from the distance map that meet the minimum distance setting are used in a watershed segmentation algorithm (Van der Walt et al., 2014) to segment and count the objects. 3). While creating multiple regions of interest (ROI) to demarcate each area containing an individual plant/target is an option, we developed two modules, cluster_contours and cluster_contours_split_img, that allow contours to be clustered and then parsed into multiple images without having to manually create multiple ROIs (Fig. The latest version or a specific release of PlantCV can be cloned from GitHub. This is a challenging area of research because the visual definition of phenotypes vary depending on the target species. 3). Preliminary evidence from a water limitation experiment performed using a Setaria recombinant inbred population indicates that vertical distance from rescaled leaf tip points identified by the acute_vertex function to the centroid is decreased in response to water limitation and thus may provide a proximity measurement of plant turgor pressure (Figs. To extend PlantCV beyond quantification of size-based morphometric features, we developed several landmarking functions. The number of rows and columns approximate the desired size of the grid cells. Modifying the stepwise input shifts the distance calculation along the x-axis, which subsequently calculates a new threshold value to use. These sixty points located along each axis possess the properties of semi/pseudo-landmark points (an equal number of reference points that are approximately geometrically homologous between subjects to be compared) that approximate the contour and shape of the object (Fig. Additionally, the use of Python allows extension of PlantCV with the many tools available from the Python scientific computing community (Oliphant, 2007; Millman & Aivazis, 2011). Department of Plant Biology, Ecology, and Evolution, Oklahoma State University, Computational and Systems Biology Program, Washington University in St. Louis, Arkansas Biosciences Institute, Arkansas State University, Arkansas Biosciences Institute, Department of Chemistry and Physics, Arkansas State University, Missouri University of Science and Technology, Department of Plant Biology, University of Georgia, Department of Agronomy and Horticulture, Center for Plant Science Innovation, Beadle Center for Biotechnology, University of Nebraska - Lincoln, This is an open access article distributed under the terms of the.

and will receive updates in the daily or weekly email digests if turned on. The field of digital plant phenotyping is at an exciting stage of development where it is beginning to shift from a bottleneck to one that will have a positive impact on plant research, especially in agriculture. The PlantCV SQLite database schema was simplified so that new tables do not need to be added for every new camera system (Fig. Note: You are now also subscribed to the subject areas of this publication Version 1.0 of PlantCV (PlantCV v1.0) was released in 2015 alongside the introduction of the Bellwether Phenotyping Facility at the Donald Danforth Plant Science Center (Fahlgren et al., 2015). If images are captured in a greenhouse, growth chamber, or other situation where light intensity is variable, image segmentation based on global thresholding of image intensity values can become variable. The output of image files mainly used to assess image segmentation quality is now optional, which should generally increase computing performance. Graphs were produced using Matplotlib v2.0.2 (Hunter, 2007) and ggplot2 v2.2.1 (Wickham, 2009). With the cluster_contour_split_img function, a text file with genotype names can be included to add them to image names. no more than one email per day or week based on your preferences. Resizing values are determined by measuring the same reference object in an example image taken from both VIS and NIR cameras (for example the width of the pot or pot carrier in each image). Once the training table is generated, it is input into the plantcv-train.py script to generate PDFs for each class. However, fully automated segmentation of individual organs such as leaves remains a challenge, due to issues such as occlusion (Scharr et al., 2016). (B) The cluster_contours_split_img function was used to split the full image into individual plants. The focus of the paper associated with the original release of PlantCV v1.0 (Fahlgren et al., 2015) was not the structure and function of PlantCV for image analysis, but rather an example of the type of biological question that can be answered with high-throughput phenotyping hardware and software platforms.

Here we present the details and rationale for major developments in the second major release of PlantCV. The location of landmark points can be used to examine multidimensional growth curves for a broad variety of study systems and tissue types and can be used to compare properties of plant shape throughout development or in response to differences in plant growth environment. Naive Bayes segmentation enabled use of pipelines that were both simpler (fewer steps) and more flexible: five new scripts were sufficient for processing the dataset (five categories of photo data), whereas nine threshold-based pipeline scripts had previously been required. Once images are split, they can be processed like single plant images using additional PlantCV tools (Fig. The effectiveness of continuous integration depends on having thorough unit test coverage of the PlantCV code base. When specified a priori, landmarks should be assigned to provide adequate coverage of the shape morphology across a single dimensional plane (Bookstein, 1991).

Kernel density estimation (KDE) is used to calculate a probability density function (PDF) from a vector of values for each HSV channel from each class.

For a growing plant, potential landmarks include the tips of leaves and pedicel and branch angles. (B) Overview of the structure of the SQLite database. 5A) and top-view images (R2=0.96; Fig. tritici) were acquired with a flatbed scanner. In PlantCV v2, a new metadata processing system was added to allow for flexibility in file naming both within and between experiments and systems. In cases where the auto-threshold value does not adequately separate the target object from background, the threshold can be adjusted by modifying the stepwise input. The get_nir function identifies the path of the NIR image that matches VIS image.

1A). Additionally, the identification of landmark points should be repeatable and reliable across subjects while not altering their topological positions relative to other landmark positions (Bookstein, 1991). The parallelization script also functions to manage data by consolidating measurements and metadata into an SQLite database (Fig. A recent example of this latter approach built on PlantCV, using its image preprocessing and segmentation functions alongside a modular framework for building convolutional neural networks (Ubbens & Stavness, 2017). Methods that utilize machine learning techniques are a promising approach to tackle these and other phenotyping challenges (Minervini, Abdelsamea & Tsaftaris, 2014; Singh et al., 2016; Ubbens & Stavness, 2017; Atkinson et al., 2017; Pound et al., 2017). The following are details on improvements to the structure, usability, and functionality of PlantCV since the v1.0 release. 5B). The pixel area of the marker is returned as a value that can be used to normalize measurements to the same scale. Therefore, software tools needed to process high-throughput image data need to be flexible and amenable to community input. Several updates to PlantCV v2 addressed the need to increase the flexibility of PlantCV to analyze data from other plant phenotyping systems. New data sources: Handling and analysis of data from specialized cameras that measure three-dimensional structure or hyperspectral reflectance will require development or integration of additional methods into PlantCV.

The current method for multi-plant identification in PlantCV is flexible but relies on a grid arrangement of plants, which is common for controlled-environment-grown plants.

In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning. See the online documentation for an example multi-plant imaging pipeline (http://plantcv.readthedocs.io/en/latest/multi-plant_tutorial/). The PlantCV metadata processing system is part of the parallelization tool and works by using a user-provided template to process filenames. PlantCV can be used to generate binary masks for the training set using the standard image processing methods and the new output_mask function. Segmented objects are visualized in different colors, and the number of segmented objects is reported (Fig. The multiclass naive Bayes approach requires a tab-delimited table for training where each column is a class (minimum two) and each cell is a comma-separated list of RGB pixel values from the column class.

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