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TECHNOLOGY AND CODE article

Front. Plant Sci.
Sec. Plant Bioinformatics
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1475057
This article is part of the Research Topic Recent Advances in Big Data, Machine, and Deep Learning for Precision Agriculture, Volume II View all 11 articles

StatFaRmer: Cultivating Insights with an Advanced R Shiny Dashboard for Digital Phenotyping Data Analysis

Provisionally accepted
Daniil Ulyanov Daniil Ulyanov 1*Alana Ulyanova Alana Ulyanova 1Dmitry Litvinov Dmitry Litvinov 1Alina Kocheshkova Alina Kocheshkova 1Nadejda Syedina Nadejda Syedina 1,2Mikhail Divashuk Mikhail Divashuk 1Gennady I. Karlov Gennady I. Karlov 1
  • 1 All-Russia Research Institute of Agricultural Biotechnology, Moscow, Russia
  • 2 Department of Biological and Medical Physics, School of Biological and Medical Physics, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Oblast, Russia

The final, formatted version of the article will be published soon.

    Digital phenotyping is a fast-growing area of hardware and software research and development. Phenotypic studies usually require determining whether there is a difference in some trait between plants with different genotypes or under different conditions. We developed StatFaRmer, a user-friendly tool tailored for analyzing time series of plant phenotypic parameters, ensuring seamless integration with common tasks in phenotypic studies. For maximum versatility across phenotypic methods and platforms, it uses data in the form of a set of spreadsheets (XLSX and CSV files). StatFaRmer is designed to handle measurements that have variation in timestamps between plants and the presence of outliers, which is common in digital phenotyping. Data preparation is automated and welldocumented, leading to customizable ANOVA tests that include diagnostics and significance estimation for effects between user-defined groups. Users can download the results from each stage and reproduce their analysis. It was tested and shown to work reliably for large datasets across various experimental designs with a wide range of plants, including bread wheat (Triticum aestivum), durum wheat (Triticum durum), and triticale (× Triticosecale); sugar beet (Beta vulgaris), cocklebur (Xanthium strumarium) and lettuce (Lactuca sativa), corn (Zea mays) and sunflower (Helianthus annuus), and soybean (Glycine max). StatFaRmer is created as an open-source Shiny dashboard, and simple instructions on installation and operation on Windows and Linux are provided.

    Keywords: high-throughput plant phenotyping1, phenotypic data visualization2, time series analysis3, digital phenotyping platforms4, genotype-phenotype analysis5, statistical analysis of phenotypic data6, open-source software7, automated data analysis8

    Received: 02 Aug 2024; Accepted: 30 Jan 2025.

    Copyright: © 2025 Ulyanov, Ulyanova, Litvinov, Kocheshkova, Syedina, Divashuk and Karlov. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Daniil Ulyanov, All-Russia Research Institute of Agricultural Biotechnology, Moscow, Russia

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.