DiSCount: computer vision for automated quantification of Striga seed germination

Background. Plant parasitic weeds belonging to the genus Striga are major threats 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 mm), 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 fiber filter assays. The model was built-up using a machine learning approach trained with a data set of 98 manually annotated images. We validated and tested the model against a total data set 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 data set. Most importantly, DiSCount present a 100-fold speed increase on CPU and 3000-fold speed increase in image analysis when compared to manual analysis, with an inference time of approximately 3 seconds per image on CPU and 0.1 seconds on CPU.

Conclusions. DiSCount is accurate and efficient in quantifying total and germinated Striga seeds in a standardized assay. This automated computer vision tool enables for high-throughput, large-scale screening of chemical compound libraries and biological control agents of an important parasitic weed in agricultural settings. The complete software and manual are hosted at https://gitlab.com/lodewijk-track32/discount_paper. The data set used for testing is available with a DOI in Zenodo.