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. It is mostly rhizome propagated; however, the increasing use of seed is resulting in a greater need to investigate germination. Miscanthus seed are small, germination is often poor and carried out without sterilisation; therefore, automated methods applied to germination detection must be able to cope with, for example, thresholding of small objects, low germination frequency and the presence or absence of mould.

Results: Machine learning using k -NN improved the scoring of different phenotypes encountered in Miscanthus seed. 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 0.69-0.7, 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. The method compared favourably to an established technique.

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.