ClearedLeavesDB: an online database of cleared plant leaf images

Abstract

BackgroundLeaf vein networks are critical to both the structure and function of leaves. A growing body of recent work has linked leaf vein network structure to the physiology, ecology and evolution of land plants. In the process, multiple institutions and individual researchers have assembled collections of cleared leaf specimens in which vascular bundles (veins) are rendered visible. In an effort to facilitate analysis and digitally preserve these specimens, high-resolution images are usually created, either of entire leaves or of magnified leaf subsections. In a few cases, collections of digital images of cleared leaves are available for use online. However, these collections do not share a common platform nor is there a means to digitally archive cleared leaf images held by individual researchers (in addition to those held by institutions). Hence, there is a growing need for a digital archive that enables online viewing, sharing and disseminating of cleared leaf image collections held by both institutions and individual researchers.DescriptionThe Cleared Leaf Image Database (ClearedLeavesDB), is an online web-based resource for a community of researchers to contribute, access and share cleared leaf images. ClearedLeavesDB leverages resources of large-scale, curated collections while enabling the aggregation of small-scale collections within the same online platform. ClearedLeavesDB is built on Drupal, an open source content management platform. It allows plant biologists to store leaf images online with corresponding meta-data, share image collections with a user community and discuss images and collections via a common forum. We provide tools to upload processed images and results to the database via a web services client application that can be downloaded from the database.ConclusionsWe developed ClearedLeavesDB, a database focusing on cleared leaf images that combines interactions between users and data via an intuitive web interface. The web interface allows storage of large collections and integrates with leaf image analysis applications via an open application programming interface (API). The open API allows uploading of processed images and other trait data to the database, further enabling distribution and documentation of analyzed data within the community. The initial database is seeded with nearly 19,000 cleared leaf images representing over 40 GB of image data. Extensible storage and growth of the database is ensured by using the data storage resources of the iPlant Discovery Environment. ClearedLeavesDB can be accessed at http://clearedleavesdb.org.

Crop Phenomics: Current Status and Perspectives

“…In 2010, PODD was developed for capturing, managing, annotating and distributing the data to support both Australian and international biological research communities (Li et al, 2010); Fabre et al (2011) built a PHENOPSIS DB information system for Arabidopsis thaliana phenotypic data acquired by the PHENOPSIS phenotyping platform; Bisque is the first web based, cross-platform, developed into a repository to store, visualize, organize and analyze images in the cloud (Kvilekval Das et al, 2010;Goff et al, 2011). In 2014, ClearedLeaves DB, an on open online database, was built to store, manage and access leaf images and phenotypic data (Das et al, 2014); AraPheno 1 was the first comprehensive, central repository of population-scale phenotypes (it integrated more than 250 publicly available phenotypes from six independent studies) for A. thaliana inbred lines (Seren et al, 2017); PhenoFront was a publicly available dataset of above-ground plant tissue to the LemnaTec Phenotyper platform (Fahlgren et al, 2015a); in 2016, the plant genomics and phenomics research data repository (PGP) were developed by the Leibniz institute of plant genetics and crop plant research and the German plant phenotyping network to comprehensively publish plant phenotypic and genotypic data (Arend et al, 2016a). Obviously, from the perspective of database data standardization and storage, the storage scheme based on "cloud technology" is becoming the trend for the development of plant phenotype data storage.…”

Taxonomic revision of the J. Vicente collection dicotyledon leaves from the lower Maastrichtian of Isona (northeastern Iberia)

“…One hundred eleven specimens from the Vicente collection were previously examined by Marmi et al (2014) following the methods of Ellis et al (2009), resulting in a total of 15 morphotypes. In the present paper, leaf features of these morphotypes are des cribed with more detail and compared with cleared leaves of the National Cleared Leaf Collection-Wolfe (NCLC-W; http://clearedleavesdb.org/) (Das et al, 2014). This database contains up to 18,126 cleared leaf images representing most of extant angiosperm families.…”

Automated and accurate segmentation of leaf venation networks via deep learning

“…For example, the LeafVeinCNN may have limited performance on lower-resolution images that are typical for digitized historic cleared leaf collections, e.g. the Smithsonian / Wolfe collection (Lobet et al, 2013;Das et al, 2014), without additional training, or using approaches such as generative adversarial networks (GANs) to refine segmentations and avoid unlikely predictions that have large blotches or disconnected areas.…”