A. Kena, Ebenezer Ogoe, Clara Cruet-Burgos, Richard Agyare, Naomi Adoma, Benjamin Annor, Rubi Raymundo, Geoffrey Morris
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引用次数: 0
摘要
农业试验、数字数据收集和高通量表型分析等现代工具的出现,要求田间小块标签既能由机器读取,也能由人工读取。这些标签通常使用商业软件制作,而发展中国家资金不足的研究项目往往无法使用这些软件。向发展中国家资金不足的研究项目提供免费的适用标签设计软件,将解决农业研究现代化的主要障碍之一。我们的目标是开发一种新的开放源码软件,其设计功能非常适合田间试验和其他农业试验。我们在此报告的 qrlabelr 是一款用于创建可打印的地块标签的新软件,它建立在现有开源程序的基础之上。qrlabelr 软件在标签设计步骤中提供了更大的灵活性,保证了 QR 编码后字符串的真实性,并为用户提供了更快的标签生成速度。新软件以 R 软件包的形式提供,并为生成绘图标签提供可定制的功能。对于非 R 用户或 R 编程初学者,该软件包提供了一个交互式 Shiny 应用程序版本,可从本地 R 启动或从 https://bit.ly/3Sud4xy 在线访问。这一新程序的设计理念强调采用地块标签设计的最佳实践,以提高农业研发研究的可重复性、可追踪性和数据整理的准确性。
Introducing qrlabelr: Fast user-friendly software for machine- and human-readable labels in agricultural research and development
The advent of modern tools in agricultural experiments, digital data collection, and high-throughput phenotyping have necessitated field plot labels that are both machine- and human-readable. Such labels are usually made with commercial software, which are often inaccessible to under-funded research programs in developing countries. The availability of free fit-for-purpose label design software to under-funded research programs in developing countries would address one of the main roadblocks to modernizing agricultural research. The goal was to develop a new open-source software with design features well-suited for field trials and other agricultural experiments. We report here qrlabelr, a new software for creating print-ready plot labels that builds on the foundation of an existing open-source program. The qrlabelr software offers more flexibility in the label design steps, guarantees true string fidelity after QR encoding, and provides faster label generation to users. The new software is available as an R package and offers customizable functions for generating plot labels. For non-R users or beginners in R programming, the package provides an interactive Shiny app version that can be launched from R locally or accessed online at https://bit.ly/3Sud4xy. The design philosophy of this new program emphasizes the adoption of best practices in plot label design to enhance reproducibility, tracking, and accurate data curation in agricultural research and development studies.