Awty-Carroll, D, Clifton-Brown, J and Robson, P (2018) Using k-NN to analyse images of diverse germination phenotypes and detect single seed germination in Miscanthus sinensis. Plant Methods, 14 (1). ISSN 1746-4811

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Bio-energy; Classification; Germination; Image analysis; Machine learning; Miscanthus; Robust classification; Seed; Seed imaging; k-NN; 0601 Biochemistry and Cell Biology; 0607 Plant Biology; 1001 Agricultural Biotechnology; Plant Biology & Botany
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > QH Natural history > QH301 Biology S Agriculture > S Agriculture (General)
Divisions:	Biological & Environmental Sciences (from Sep 19)
Publisher:	Springer Science and Business Media LLC
SWORD Depositor:	A Symplectic
Date Deposited:	10 Jan 2023 14:46
Last Modified:	10 Jan 2023 14:46
DOI or ID number:	10.1186/s13007-018-0272-0
URI:	https://researchonline.ljmu.ac.uk/id/eprint/18585

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