title: Phenotiki: an open software and hardware platform for affordable and easy image-based phenotyping of rosette-shaped plants.
abstract: Phenotyping is important to understand plant biology, but current solutions are costly, not versatile or are difficult to deploy. To solve this problem, we present Phenotiki, an affordable system for plant phenotyping that, relying on off-the-shelf parts, provides an easy to install and maintain platform, offering an out-of-box experience for a well-established phenotyping need: imaging rosette-shaped plants. The accompanying software (with available source code) processes data originating from our device seamlessly and automatically. Our software relies on machine learning to devise robust algorithms, and includes an automated leaf count obtained from 2D images without the need of depth (3D). Our affordable device (~€200) can be deployed in growth chambers or greenhouse to acquire optical 2D images of approximately up to 60 adult Arabidopsis rosettes concurrently. Data from the device are processed remotely on a workstation or via a cloud application (based on CyVerse). In this paper, we present a proof-of-concept validation experiment on top-view images of 24 Arabidopsis plants in a combination of genotypes that has not been compared previously. Phenotypic analysis with respect to morphology, growth, color and leaf count has not been performed comprehensively before now. We confirm the findings of others on some of the extracted traits, showing that we can phenotype at reduced cost. We also perform extensive validations with external measurements and with higher fidelity equipment, and find no loss in statistical accuracy when we use the affordable setting that we propose. Device set-up instructions and analysis software are publicly available ( http://phenotiki.com).
authors.affiliation: IMT School for Advanced Studies, Piazza S. Francesco 19, 55100, Lucca, Italy.
authors.forename: Massimo
authors.identifier: http://orcid.org/0000-0003-2613-7964
authors.initials: M
authors.lastname: Minervini
authors.affiliation: IMT School for Advanced Studies, Piazza S. Francesco 19, 55100, Lucca, Italy.|Institute for Digital Communications, School of Engineering, University of Edinburgh, Thomas Bayes Road, EH9 3FG, Edinburgh, UK.|The Alan Turing Institute, 96 Euston Road, NW1 2DB, London, UK.
authors.forename: Mario V
authors.initials: MV
authors.lastname: Giuffrida
authors.affiliation: PlantLab, Institute of Life Sciences, Scuola Superiore Sant'Anna, Via Mariscoglio 34, 56124, Pisa, Italy.
authors.forename: Pierdomenico
authors.initials: P
authors.lastname: Perata
authors.affiliation: Institute for Digital Communications, School of Engineering, University of Edinburgh, Thomas Bayes Road, EH9 3FG, Edinburgh, UK.
authors.forename: Sotirios A
authors.initials: SA
authors.lastname: Tsaftaris
mesh_terms: D000465:Algorithms; D007091:Image Processing, Computer-Assisted; D000069550:Machine Learning; D010641:Phenotype; D018515:Plant Leaves / Q000033:anatomy & histology / Q000378:metabolism; D010944:Plants / Q000033:anatomy & histology* / Q000145:classification*; D012984:Software
keywords: 
  Arabidopsis thaliana
  ; Raspberry Pi; affordable; growth; image analysis; phenotyping; software; technical advance