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Sorghum is a major staple crop in sub-Saharan Africa with yields severely impacted by biotic and abiotic factors. Here, we analysed the taxonomic diversity and biogeographical distribution of bacterial taxa of 48 agricultural fields along a transect of approximately 2000 km across the Ethiopian sorghum belt, the centre of origin of sorghum. The ultimate goal is to identify—yet-unexplored—beneficial plant–microbe associations. Based on bulk soil bacterial communities and DArT-SNP analyses of 59 sorghum accessions, we selected three microbiologically distinct field soils and 12 sorghum genotypes, including commercial varieties, wild relatives, and farmer-preferred landraces. The results showed a core rhizosphere microbiome of 2125 amplicon sequence variants (ASVs), belonging to eight bacterial families consistently found across the three soil types and the 12 sorghum genotypes. Integration of the rhizosphere bacterial community analysis with DArT-SNP sorghum genotyping revealed the association of differentially abundant ASVs with sorghum genotypic traits, including the distinct recruitment of Pseudomonadaceae by the stay-green, drought-tolerant, and wild sorghum genotypes. Collectively, these results provide new insights into the core and accessory bacterial taxa in the sorghum rhizosphere in the centre of origin, setting a baseline for targeted isolation and functional characterization of putative beneficial rhizobacteria. [ABSTRACT FROM AUTHOR]

Background: Plant parasitic weeds belonging to the genus Striga are a major threat for food production in Sub-Saharan Africa and Southeast Asia. The parasite's life cycle starts with the induction of seed germination by host plant-derived signals, followed by parasite attachment, infection, outgrowth, flowering, reproduction, seed set and dispersal. Given the small seed size of the parasite (< 200 μm), quantification of the impact of new control measures that interfere with seed germination relies on manual, labour-intensive counting of seed batches under the microscope. Hence, there is a need for high-throughput assays that allow for large-scale screening of compounds or microorganisms that adversely affect Striga seed germination. Results: Here, we introduce DiSCount (Digital Striga Counter): a computer vision tool for automated quantification of total and germinated Striga seed numbers in standard glass fibre filter assays. We developed the software using a machine learning approach trained with a dataset of 98 manually annotated images. Then, we validated and tested the model against a total dataset of 188 manually counted images. The results showed that DiSCount has an average error of 3.38 percentage points per image compared to the manually counted dataset. Most importantly, DiSCount achieves a 100 to 3000-fold speed increase in image analysis when compared to manual analysis, with an inference time of approximately 3 s per image on a single CPU and 0.1 s on a GPU. Conclusions: DiSCount is accurate and efficient in quantifying total and germinated Striga seeds in a standardized germination assay. This automated computer vision tool enables for high-throughput, large-scale screening of chemical compound libraries and biological control agents of this devastating parasitic weed. The complete software and manual are hosted at https://gitlab.com/lodewijk-track32/discount%5fpaper and the archived version is available at Zenodo with the DOI 10.5281/zenodo.3627138. The dataset used for testing is available at Zenodo with the DOI 10.5281/zenodo.3403956. [ABSTRACT FROM AUTHOR]