Wisconsin diversity panel phenotypes: spoken descriptions of plants and supporting data

Authors: YANARELLA, COLLEEN - Iowa State University; FATTEL, LEILA - Iowa State University; KRISTMUNDSDOTTIR, ASRUN - Iowa State University; Lopez, Miriam; Edwards, Jode; CAMPBELL, DARWIN - Iowa State University; Abel, Craig; LAWRENCE-DILL, CAROLYN - Iowa State University

Submitted to: BMC Research Notes
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/11/2024
Publication Date: 1/23/2024
Citation: Yanarella, C.F., Fattel, L., Kristmundsdottir, A.Y., Lopez, M.D., Edwards, J.W., Campbell, D.A., Abel, C.A., Lawrence-Dill, C.J. 2024. Wisconsin diversity panel phenotypes: spoken descriptions of plants and supporting data. BMC Research Notes. 17. Article 33. https://doi.org/10.1186/s13104-024-06694-y.
DOI: https://doi.org/10.1186/s13104-024-06694-y

Interpretive Summary: For centuries, indigenous and modern corn breeders have used trait-assisted decision making towards breeding corn with desirable qualities and high yield. This work employs both novel and traditional methods of gathering plant phenotypic data. The study made use of a diverse set of corn lines to produce a dataset of descriptions of plant features (phenotypes) using recordings of spoken natural language. In addition, this dataset includes traditional phenotyping scores and measurements, as well as photographs (both aerial and at plant level). The data produced by this study has been made available for further study. The unique nature of the data produced in this work allows for many types of analyses including machine learning and computer assisted selection for breeding. The goal is continued improvement of maize that will withstand and adapt to challenges such as drought, pathogens and herbivores to provide a safe and reliable food source for the growing world population.

Technical Abstract: Phenotyping plants in a field environment can involve a variety of methods including the use of automated instruments and labor-intensive manual measurement and scoring. Researchers also collect language-based phenotypic descriptions and use controlled vocabularies and structures such as ontologies to enable computation on descriptive phenotype data, including methods to determine phenotypic similarities. In this study, spoken descriptions of plants were collected and observers were instructed to use their own vocabulary to describe plant features that were present and visible. Further, these plants were measured and scored manually as part of a larger study to investigate whether spoken plant descriptions can be used to recover known biological phenomena. Data comprise phenotypic observations of 686 accessions of the maize Wisconsin Diversity panel, and 25 positive control accessions that carry visible, dramatic phenotypes. The data include the list of accessions planted, field layout, data collection procedures, student participants’ and volunteers’ observation transcripts, volunteers’ audio data files, terrestrial and aerial images of the plants, Amazon Web Services method selection experimental data, and manually collected (measurements and scores) phenotypes (e.g., plant height, ear and tassel features, etc.). Data were collected during the summer of 2021 at Iowa State University's Agricultural Engineering and Agronomy Research Farms.