Using k-NN to analyse images of diverse germination phenotypes and detect single seed germination in Miscanthus sinensis

Background: Miscanthus is a leading second generation bio-energy crop, which is currently planted using rhizomes; however, increasingly the use of seed is being explored to improve efficiency of propagation. Miscanthus seed are small, germination is often poor and without sterilisation so germination detection must be sufficiently adaptable for example to the presence or absence of mould.

Results: Machine learning using k-NN improved the scoring of different seed phenotypes encountered in scoring germination for Miscanthus. The k-NN-based algorithm was effective in scoring the germination of seed images when compared with human scores of the same images. The trueness of the k-NN result was 0.69 to 0.7, as measured using the area under a ROC curve. When the k-NN classifier was tested on an optimised image subset of seed an area under the ROC curve of 0.89 was achieved.

Conclusions: With non-ideal seed images that included mould and broken seed the k-NN classifier was less consistent with human assessments. The most accurate assessment of germination with which to train classifiers is difficult to determine but the k-NN classifier provided an impartial consistent measurement of this important trait. It was more reproducible than the existing human scoring methods and was demonstrated to give a high degree of trueness to the human score.