Raspberry Pi–powered imaging for plant phenotyping. (31st March 2018)

• Record Type: Journal Article
• Authors: Tovar, Jose C.; Hoyer, J. Steen; Lin, Andy; Tielking, Allison; Callen, Steven T.; Elizabeth Castillo, S.; Miller, Michael; Tessman, Monica; Fahlgren, Noah; Carrington, James C.; Nusinow, Dmitri A.; Gehan, Malia A.
• Is Part Of: Applications in plant sciences. Volume 6:Number 3(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-03-31
• Subjects: imaging -- low‐cost phenotyping -- morphology -- Raspberry Pi; Plants -- Periodicals; Plant physiology -- Periodicals; Plant Physiological Phenomena; Plant physiology; Plants; Periodicals; Periodicals; Fulltext; Internet Resources; Periodicals; 580
• DOI: 10.1002/aps3.1031
• Languages: English
• ISSNs: 2168-0450

Abstract

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.