The ‘PhenoBox’, a flexible, automated, open‐source plant phenotyping solution

Abstract

SummaryThere is a need for flexible and affordable plant phenotyping solutions for basic research and plant breeding.We demonstrate our open source plant imaging and processing solution ('PhenoBox'/ 'PhenoPipe') and provide construction plans, source code and documentation to rebuild the system. Use of the PhenoBox is exemplified by studying infection of the model grass Brachypodium distachyon by the head smut fungus Ustilago bromivora, comparing phenotypic responses of maize to infection with a solopathogenic Ustilago maydis (corn smut) strain and effector deletion strains, and studying salt stress response in Nicotiana benthamiana.In U. bromivora-infected grass, phenotypic differences between infected and uninfected plants were detectable weeks before qualitative head smut symptoms. Based on this, we could predict the infection outcome for individual plants with high accuracy. Using a PhenoPipe module for calculation of multi-dimensional distances from phenotyping data, we observe a time after infection-dependent impact of U. maydis effector deletion strains on phenotypic response in maize. The PhenoBox/PhenoPipe system is able to detect established salt stress responses in N. benthamiana.We have developed an affordable, automated, open source imaging and data processing solution that can be adapted to various phenotyping applications in plant biology and beyond.

Classifying cold stress responses of inbred maize seedlings using RGB imaging

“…Many researchers have, or are currently developing, custom low-cost phenotyping platforms to fit their needs. Our system is not necessarily unique in this pursuit and is similar to other recently developed low-cost imaging systems (Knecht et al, 2016; Armoniené et al, 2018; Czedik-Eysenberg et al, 2018). Currently, our system is limited to acquiring images of seedlings from one side view image but has the advantage of being scalable.…”

Classifying cold‐stress responses of inbred maize seedlings using RGB imaging

“…Many researchers have, or are currently developing, custom low-cost phenotyping platforms to fit their needs. Our system is not necessarily unique in this pursuit and is similar to other recently developed low-cost imaging systems (Armoniené, Odilbekov, Vivekanand, & Chawade, 2018;Czedik-Eysenberg et al, 2018;Knecht, Campbell, Caprez, Swanson, & Walia, 2016). Currently, our system is limited to acquiring images of seedlings from one side view image but has the advantage of being scalable.…”

Rapid gene cloning in cereals

“…In parallel, phenotyping systems are being developed for small-scale greenhouse (e.g. Hartmann et al 2011, Clark et al 2013, and Czedik-Eysenberg et al 2018 to large-scale field applications (e.g. Crain et al 2016, Haghighattalab et al 2016, and Virlet et al 2017.…”

“…These fungal pathogens produce obvious spore-filled sori in floral organs, but do not cause obvious symptoms during the plant's vegetative phase. Czedik-Eysenberg et al (2018) developed an automated phenotyping system that is able to detect subtle growth differences of infected plants prior to flowering and reliably predict infection outcome.…”