similar vantage point (a 4 × 5 grid of pots) in each field of view, such that very similar computa- tional pipelines can be used to process images from all 12 cameras. An example image has been processed with PlantCV (Fahlgren et al., 2015) in Fig. 2, and a script showing and describing each step in the analysis is provided at https://github.com/danforthcenter/apps-phenotyping. Further image- processing tutorials and tips can be found at http://plantcv.readthedocs.io/en/latest/.Raspberry Pi camera stand An adjustable camera stand is a versatile piece of laboratory equip- ment for consistent imaging. Appendix 3 is a protocol for pairing a low-cost home-built camera stand with a Raspberry P
Open resource ↗danforthcenter/apps-phenotyping · pdf-raw-page:3 lines:1-86Paper record
Raspberry Pi-powered imaging for plant phenotyping.
Applications in Plant Sciences · 1 Mar 2018 · 10.1002/aps3.1031
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.
Code and data availability