Raspberry Pi-powered imaging for plant phenotyping.

Abstract

PREMISE OF THE STUDY: Image-based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent image acquisition easy are often cost-prohibitive. To make high-throughput phenotyping methods more accessible, low-cost microcomputers and cameras can be used to acquire plant image data. METHODS AND RESULTS: We used low-cost Raspberry Pi computers and cameras to manage and capture plant image data. Detailed here are three different applications of Raspberry Pi-controlled imaging platforms for seed and shoot imaging. Images obtained from each platform were suitable for extracting quantifiable plant traits (e.g., shape, area, height, color) en masse using open-source image processing software such as PlantCV. CONCLUSIONS: This protocol describes three low-cost platforms for image acquisition that are useful for quantifying plant diversity. When coupled with open-source image processing tools, these imaging platforms provide viable low-cost solutions for incorporating high-throughput phenomics into a wide range of research programs.

Plant phenotyping relevance

低コストの画像取得プラットフォームを開発・記述し、植物形質の定量化に適用した方法論研究である。

Code and data availability

The paper provides public author analysis scripts (PlantCV-based phenotyping pipelines for Arabidopsis, quinoa seeds, and quinoa plants) hosted on the authors' GitHub repository, explicitly linked in the text and appendices.