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Raspberry Pi Powered Imaging for Plant Phenotyping

Tovar, J. · Hoyer, J. S. · Lin, A. · Tielking, A. · Callen, S. · Castillo, E. · Miller, M. · Tessman, M. · Fahlgren, N. · Carrington, J. · Nusinow, D. · Gehan, M. A.

bioRxiv · 1 Sept 2017 · 10.1101/183822

Abstract

O_LIPremise 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.\nC_LIO_LIMethods 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 (shape, area, height, color) en masse using open-source image processing software such as PlantCV.\nC_LIO_LIConclusion: 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.\nC_LI

Code and data availability

The paper's PlantCV image-analysis scripts for its Raspberry Pi phenotyping examples are publicly available in the authors' danforthcenter/apps-phenotyping repository. The outreach and gphoto URLs are not paper-specific analysis assets.

Codepublic

An​ ​example​ ​image​ ​has​ ​been​ p​ rocessed​ ​with​ ​PlantCV​ ​(Fahlgren​ ​et​ ​al.,​ ​2015)​,​ ​and the​ ​analysis​ ​script​ ​is​ ​available​ ​at​ ​https://github.com/danforthcenter/apps-phenotyping

Open resource ↗danforthcenter/apps-phenotyping · pdf-page:8 lines:1-52