Smartphone image capture system and image analysis pipelines enable accurate and efficient phenotyping of spaced plant mapping populations

Abstract

Plain Language Summary The lack of low-cost, user-friendly and expedient methods for plant phenotyping challenges researchers’ ability to efficiently collect accurate phenotypic data in large field experiments. Here, we demonstrate the use of a novel, smartphone-based image capture system and two user-friendly image analysis pipelines (utilizing PlantCV or Biodock AI) to increase the throughput of plant phenotyping in two large, spaced plant populations. We showed that the image capture system collected images of adequate quality for downstream analysis using either the PlantCV or Biodock AI pipeline. Both image analysis pipelines produced phenotype values in line with those obtained using manual image annotation. Together, these results provide researchers with a low-cost, user-friendly image-based phenotyping method that can be widely applied to increase phenotyping throughout in field experiments.

Code and data availability

The supplied blocks describe a smartphone image capture system and PlantCV/Biodock AI/ImageJ phenotyping pipelines, but contain no data availability statement, no public dataset/image deposit, and no author code repository or URL. The custom PlantCV scripts and trained Biodock models are mentioned but no public access,

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