SeedGerm: a cost-effective phenotyping platform for automated seed imaging and machine-learning based phenotypic analysis of crop seed germination.

Efficient seed germination and establishment are important traits for field and glasshouse crops. Large-scale germination experiments are laborious and prone to observer errors, leading to the necessity for automated methods. We experimented with five crop species, including tomato, pepper, Brassica, barley, and maize, and concluded an approach for large-scale germination scoring. Here, we present the SeedGerm system, which combines cost-effective hardware and open-source software for seed germination experiments, automated seed imaging, and machine-learning based phenotypic analysis. The software can process multiple image series simultaneously and produce reliable analysis of germination- and establishment-related traits, in both comma-separated values (CSV) and processed images (PNG) formats. In this article, we describe the hardware and software design in detail. We also demonstrate that SeedGerm could match specialists' scoring of radicle emergence. Germination curves were produced based on seed-level germination timing and rates rather than a fitted curve. In particular, by scoring germination across a diverse panel of Brassica napus varieties, SeedGerm implicates a gene important in abscisic acid (ABA) signalling in seeds. We compared SeedGerm with existing methods and concluded that it could have wide utilities in large-scale seed phenotyping and testing, for both research and routine seed technology applications.

Zinc Finger Artificial Transcription Factor-Mediated Chloroplast Genome Interrogation in Arabidopsis thaliana.

The large majority of core photosynthesis proteins in plants are encoded by nuclear genes, but a small portion have been retained in the plastid genome. These plastid-encoded chloroplast proteins fulfill essential roles in the process of photochemistry. Here, we report the use of nuclear-encoded, chloroplast-targeted zinc finger artificial transcription factors (ZF-ATFs) with effector domains of prokaryotic origin to modulate the expression of chloroplast genes, and to enhance the photochemical activity and growth characteristics of Arabidopsis thaliana plants. This technique was named chloroplast genome interrogation. Using this novel approach, we obtained evidence that ZF-ATFs can indeed be translocated to chloroplasts of Arabidopsis plants, can modulate their growth and operating light use efficiency of PSII, and finally can induce statistically significant changes in the expression levels of several chloroplast genes. Our data suggest that the distortion of chloroplast gene expression might be a feasible approach to manipulate the efficiency of photosynthesis in plants.