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Motivation Modern genomic breeding methods rely heavily on very large amounts of phenotyping and genotyping data, presenting new challenges in effective data management and integration. Recently, the size and complexity of datasets have increased significantly, with the result that data are often stored on multiple systems. As analyses of interest increasingly require aggregation of datasets from diverse sources, data exchange between disparate systems becomes a challenge. Results To facilitate interoperability among breeding applications, we present the public plant Breeding Application Programming Interface (BrAPI). BrAPI is a standardized web service API specification. The development of BrAPI is a collaborative, community-based initiative involving a growing global community of over a hundred participants representing several dozen institutions and companies. Development of such a standard is recognized as critical to a number of important large breeding system initiatives as a foundational technology. The focus of the first version of the API is on providing services for connecting systems and retrieving basic breeding data including germplasm, study, observation, and marker data. A number of BrAPI-enabled applications, termed BrAPPs, have been written, that take advantage of the emerging support of BrAPI by many databases. Availability and implementation More information on BrAPI, including links to the specification, test suites, BrAPPs, and sample implementations is available at https://brapi.org/. The BrAPI specification and the developer tools are provided as free and open source.

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High-throughput image-phenotyping promises to accelerate the rate of genetic improvement in plant breeding through varietal selections informed by longitudinal growth models. To facilitate routine analyses and to drive breeding decisions, data integration is critical for effective management of germplasm, field experiment design, phenotyping, tissue sampling, genotyping, aerial-phenotyping campaigns, image files, and geo-spatial information. To this end, ImageBreed provides a software solution for end-to-end image-based phenotyping integrated into the Breedbase plant breeding system. ImageBreed provides open-source orthophotomosaic construction for raw image captures from standard color cameras and from the MicaSense Red-Edge multispectral camera. Additionally, previously assembled orthophotomosaic raster images can be uploaded. Orthophotomosaic images allow for streamlined extraction of plot-polygon images; however, ImageBreed plot-polygon images can also be extracted directly from raw aerial image captures. A web-database interface streamlines assignment of plot-polygon images from the orthophotomosaic or raw aerial-captures to the field experiment design. Image processes spanning Fourier-transform filtering, thresholding, and vegetation index masking are applied to reduce noise in extracted phenotypes. Summary-statistic phenotypic values are extracted for every observed plot-polygon image using a structured ontology. Plot-polygon images are queryable against genotypic, phenotypic, and experimental design information for training of machine learning models and Abbreviations: API, application programming interface; CNN, convolutional neural network; FT-HPF, Fourier transform high-pass filter; FT-LPF, Fourier transform low-pass filter; ND, Chado Natural Diversity Database Schema; NDRE, normalized difference red-edge vegetation index; NDVI, normalized difference vegetation index; TGI, triangular greenness index; UAV, unoccupied aerial vehicle; VARI, visible atmospherically resistant index. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.