Automated plant phenotyping system for time-course monitoring of abiotic stress responses
An affordable, scalable imaging platform that tracks Arabidopsis growth and color changes over time following abiotic stress events. Uses top-down imaging with QR-coded metadata and open-source image analysis to reveal stress resilience traits that single time-point studies miss, applicable across plant species and research programs.
Overview
This solution is a low-cost, high-throughput phenotyping system designed to capture dynamic plant growth and physiological changes following abiotic stress events. Unlike conventional approaches that measure stress responses at a single time point and typically focus on seedlings, this platform enables continuous, non-destructive monitoring of adult plants over time. By analyzing time-course data, the system aims to uncover new genetic loci contributing to stress resilience in Arabidopsis and other species, addressing a critical gap in plant biology research.
Technical specifications
Key features:
- Top-down imaging of individual plants with automated metadata capture via QR codes linked to each specimen
- Python-based camera control script that embeds metadata directly into image filenames for streamlined data management
- Open-source image analysis using PlantCV to extract estimated biomass and color-based traits
- Modular and adaptable design compatible with different plant species and experimental setups
- Easily parallelizable for scaling across larger experiments and populations
- Affordable hardware requirements suitable for academic labs and field stations
Technology readiness level
The system has been prototyped and actively deployed by the lab at Hofstra University, with hundreds of images collected in short daily sessions using undergraduate research support. Initial validation experiments on single-gene knockouts and natural Arabidopsis accessions have been conducted, with replication currently underway. Early findings suggest that adult-stage phenotypes may differ from published seedling-stage results, underscoring the value of time-course monitoring. The platform is ready for expanded validation and collaborative refinement, particularly in areas of modeling and statistical analysis of phenotypic data.
About Hofstra University
Hofstra University is a private, comprehensive university on Long Island that pairs broad academics with applied research and close ties to regional industry. Its integration with Northwell Health through co-located academic programs and clinical sites enables translational studies, access to patient populations, and clinician-scientist collaboration. The Fred DeMatteis School of Engineering and Applied Science runs a structured co-op program placing students with companies across Long Island and New York City, building pipelines for talent and joint problem-solving. Research is supported by competitive federal funding from agencies such as the National Institutes of Health and the National Science Foundation, with a dedicated technology transfer office facilitating IP, licensing, and industry-sponsored agreements.
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