Massimo Minervini, Mario Valerio Giuffrida, Pierdomenico Perata, Sotirios A. Tsaftaris
The Plant Journal (2017)
Massimo Minervini, Mario Valerio Giuffrida, Pierdomenico Perata, Sotirios A. Tsaftaris (2017) “Phenotiki: An open software and hardware platform for affordable and easy image-based phenotyping of rosette-shaped plants,” The Plant Journal.
Phenotyping is important to understand plant biology but current solutions are either costly, not versatile or difficult to deploy. To solve this problem, we present Phenotiki, an affordable system for plant phenotyping which, 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 automated leaf count obtained from 2D images without the need of depth (3D). Our affordable device (~200€) can be deployed in growth chambers or greenhouses 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 previously compared. Their phenotypic analysis with respect to morphology, growth, color and leaf count has not been done previously comprehensively. We confirm 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 we propose. Device setup instructions and analysis software are publicly available (http://phenotiki.com).