Our promise For this module to function correctly we assume that the size marker stays in frame, is unobstructed, and is relatively consistent in position throughout a dataset, though some movement is allowed as long as the marker remains within the defined marker region of interest. A PlantCV pipeline is written by the user as a Python script. The following are details on improvements to the structure, usability, and functionality of PlantCV since the v1.0 release. Scripts, notebooks, SQL schema, and simple input data associated with the figures and results presented in this paper are available on GitHub at https://github.com/danforthcenter/plantcv-v2-paper. If there is a conflict in the number of names and objects, a warning is printed and a correction is attempted. To improve the pipeline and function development process in PlantCV v2, the debugging system was updated to allow for seamless integration with the Juptyer Notebook system (http://jupyter.org/; Kluyver et al., 2016). Kerrigan B. Gilbert prepared figures and/or tables, reviewed drafts of the paper. The PlantCV SQLite database schema was simplified so that new tables do not need to be added for every new camera system (Fig. If a specific area is not selected then the whole image is used. There are several areas where we envision future PlantCV development. 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. and will receive updates in the daily or weekly email digests if turned on. If images are consistent, only one image needs to be sampled for generating the training table; however, if they vary, several images may be needed. 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). New utilities were added to PlantCV v2 that allow data to be quickly and efficiently exported from the SQLite database into text files that are compatible with R (R Core Team, 2017) for further statistical analysis and data visualization. The modular structure of the PlantCV package makes it easier for members of the community to become contributors. Utilizing a rectangular neighborhood around a center pixel, median_blur replaces each pixel in the neighborhood with the median value. Additionally, the use of the Python package Matplotlib (Hunter, 2007) in PlantCV v1.0 limited the number of usable processors to 1012. PlantCV v1.0 was envisioned as a base suite of tools that the community could build upon, which lead to several design decisions aimed at encouraging participation. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. Here we define high-throughput as thousands or hundreds of thousands of images per dataset. We would like to thank Melinda Darnell, Leonardo Chavez, Kevin Reilly, and the staff of both the Danforth Center Facilities and Support Services group and the Plant Growth Facility for careful maintenance of the Danforth Center phenotyping facilities. A common approach is to include metadata within image filenames, but because there is a lack of file naming standards, it can be difficult to robustly capture this data automatically. 5). Finally, the use of a permissive, open-source license (MIT) allows PlantCV to be used, reused, or repurposed with limited restrictions, for both academic and proprietary applications. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. "Following" is like subscribing to any updates related to a publication. Each channel of the image is scaled relative to the reference maximum. PlantCV v2: Image analysis software for high-throughput plant phenotyping. Additionally, PlantCV v2 includes the acute_vertex function that uses the same chain code-based pseudo-landmark identification algorithm used in the acute function except that it uses an adjustable local search space criteria to reduce the number of angle calculations, which speeds up landmark identification. Modifying the stepwise input shifts the distance calculation along the x-axis, which subsequently calculates a new threshold value to use. We currently use the Pixel Inspection Tool in ImageJ (Schneider, Rasband & Eliceiri, 2012) to collect samples of pixel RGB values used to generate the training text file. This function estimates the vertical, horizontal, Euclidean distance, and angle of landmark points from two landmarks (centroid of the plant object and centroid localized to the base of the plant). If a white color standard is visible within the image, the user can specify a region of interest. In summary, the naive Bayes classifier offers several advantages over threshold-based segmentation: (1) two or more classes can be segmented simultaneously; (2) probabilistic segmentation can be more robust across images than fixed thresholds; and (3) classifier-based segmentation replaces multiple steps in threshold-based pipelines, reducing pipeline complexity. Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. The term high-throughput is relative to the difficulty to collect the measurement. The triangle_auto_threshold function implements the method developed by Zack, Rogers & Latp (1977). The size marker function allows users to either detect a size marker within a user-defined region of interest or to select a specific region of interest to use as the size marker. 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). Image processing pipelines, which process single images (possibly containing multiple plants), can be deployed over large image sets using PlantCV parallelization, which outputs an SQLite database of both measurements and image/experimental metadata. 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. John G. Hodge and Andrew N. Doust contributed to the research described while working at the University of Oklahoma. 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. Despite the abundance of software packages, long-term sustainability of individual projects may become an issue due to the lack of incentives for maintaining bioinformatics software developed in academia (Lobet, 2017). Image analysis was done in PlantCV using Python v2.7.5, OpenCV v2.4.5 (Bradski, 2000), NumPy v1.12.1 (Van der Walt, Colbert & Varoquaux, 2011), Matplotlib v2.0.2 (Hunter, 2007), SciPy v0.19.0 (Jones, Oliphant & Peterson, 2014), Pandas v0.20.1 (McKinney, 2010), scikit-image v0.13.0 (Van der Walt et al., 2014), and Jupyter Notebook v4.2.1 (Kluyver et al., 2016). 2B). The pixel area of the marker is returned as a value that can be used to normalize measurements to the same scale. Steven T. Callen analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, reviewed drafts of the paper. 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. Such semi/pseudo-landmarking strategies have been utilized in cases where traditional homologous landmark points are difficult to assign or poorly represent the features of object shape (Bookstein, 1997; Gunz, Mitteroecker & Bookstein, 2005; Gunz & Mitteroecker, 2013). Therefore, several functions were added to allow the plant binary mask that results from VIS image processing pipelines to be resized and used as a mask for NIR images. A random sample of 10% of the foreground pixels and the same number background pixels are used to build the PDFs. Further segmentation can also be done using the average pixel values output (pt_vals) for each pseudo-landmark, which estimates the mean pixel intensity within the convex hull of each acute region based on the binary mask used in the analysis. The plant object is divided up into twenty equidistant bins, and the minimum and maximum extent of the object along the axis and the centroid of the object within each bin is calculated. PeerJ promises to address all issues as quickly and professionally as possible. In PlantCV v1.0, image analysis parallelization was achieved using a Perl-based multi-threading system that was not thread-safe, which occasionally resulted in issues with data output that had to be manually corrected. 1A). An example of one such application is the landmark_reference_pt_dist function. 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. Images that are not collected from a consistent vantage point require one or more size markers as references for absolute or relative scale. PlantCV v1.0 required pipeline development to be done using the command line, where debug mode is used to write intermediate image files to disk for each step. As noted above for the two-class approach, it is important to adequately capture the variation in the image dataset for each class when generating the training text file to improve pixel classification. 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). 4A). Several updates to PlantCV v2 addressed the need to increase the flexibility of PlantCV to analyze data from other plant phenotyping systems. The naive_bayes_classifer function uses these PDFs to calculate the probability (using Bayes theorem) that a given pixel is in each class. In addition to the code, the PlantCV documentation was enhanced to use a continuous documentation framework using the Read the Docs service (https://readthedocs.org/), which allows documentation to be updated automatically and versioned in parallel with updates to PlantCV.
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