# Introducing qrlabelr: Fast user-friendly software for machine- and human-readable labels in agricultural research and development

## TL;DR

qrlabelr is an open-source software for creating machine- and human-readable labels for agricultural research, offering flexibility and accessibility.

## Contribution

qrlabelr introduces a user-friendly, open-source solution for generating plot labels with improved design and QR encoding fidelity.

## Key findings

- qrlabelr provides faster and more flexible label generation compared to existing tools.
- The software is accessible via an R package and a Shiny app for non-R users.
- qrlabelr promotes best practices in label design for better data tracking and reproducibility.

## Abstract

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.