DiSCount: computer vision for automated quantification of Striga seed germination.

Raul MastelingLodewijk VoorhoeveJoris IJsselmuidenFrancisco Dini-AndreoteWietse de BoerJos M Raaijmakers

Published in: Plant methods (2020)

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_paper 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.

Keyphrases

high throughput
deep learning
single cell
plant growth
high resolution
machine learning
psychometric properties