Abstract
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. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. 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. Here we present the details and rationale for major developments in the second major release of PlantCV. 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.
Highlights
All approaches for improving crops eventually require measurement of traits (Fahlgren, Gehan & Baxter, 2015)
Project-specific GitHub repositories are kept separate from the Plant Computer Vision (PlantCV) software repository because their purpose is to make project-specific analyses available for reproducibility, while the main PlantCV software repository contains general purpose image analysis modules, utilities, and documentation
Images of Setaria viridis (A10) and Setaria italica (B100) are from publicly available datasets that are available at http://plantcv.danforthcenter.org/pages/data.html (Fahlgren et al, 2015; Feldman et al, 2017)
Summary
All approaches for improving crops eventually require measurement of traits (phenotyping) (Fahlgren, Gehan & Baxter, 2015). Plant phenotyping is widely recognized as a major bottleneck in crop improvement (Furbank & Tester, 2011). Targeted plant phenotypes can range from measurement of gene expression, to flowering time, to grain yield; the software and hardware tools used are often diverse. We focus on the software tools required to nondestructively measure plant traits through images. This is a challenging area of research because the visual definition of phenotypes vary depending on the target species. Identification of petals can be used to measure flowering time, but petal color can vary by species. Software tools needed to process high-throughput image data need to be flexible and amenable to community input
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