Multi-feature machine learning model for automatic segmentation of green fractional vegetation cover for high-throughput field phenotyping

A - Papers appearing in refereed journals

Sadeghi-Tehran, P., Virlet, N., Sabermanesh, K. and Hawkesford, M. J. 2017. Multi-feature machine learning model for automatic segmentation of green fractional vegetation cover for high-throughput field phenotyping. Plant Methods. 13 (103), pp. 1-16.

AuthorsSadeghi-Tehran, P., Virlet, N., Sabermanesh, K. and Hawkesford, M. J.
Abstract

Background
Accurately segmenting vegetation from the background within digital images is both a fundamental and a challenging task in phenotyping. The performance of traditional methods is satisfactory in homogeneous environments, however, performance decreases when applied to images acquired in dynamic field environments.

Results
In this paper, a multi-feature learning method is proposed to quantify vegetation growth in outdoor field conditions. The introduced technique is compared with the state-of the-art and other learning methods on digital images. All methods are compared and evaluated with different environmental conditions and the following criteria: (1) comparison with ground-truth images, (2) variation along a day with changes in ambient illumination, (3) comparison with manual measurements and (4) an estimation of performance along the full life cycle of a wheat canopy.

Conclusion
The method described is capable of coping with the environmental challenges faced in field conditions, with high levels of adaptiveness and without the need for adjusting a threshold for each digital image. The proposed method is also an ideal candidate to process a time series of phenotypic information throughout the crop growth acquired in the field. Moreover, the introduced method has an advantage that it is not limited to growth measurements only but can be applied on other applications such as identifying weeds, diseases, stress, etc.

KeywordsField phenotyping; Fractional cover ; Learning-based segmentation ; Field Scanalyzer ; RGB images
Year of Publication2017
JournalPlant Methods
Journal citation13 (103), pp. 1-16
Digital Object Identifier (DOI)doi:10.1186/s13007-017-0253-8
PubMed ID29201134
Open accessPublished as ‘gold’ (paid) open access
FunderBiotechnology and Biological Sciences Research Council
Funder project or codeDesigning Future Wheat (DFW) [ISPG]
DFW - Designing Future Wheat - Work package 1 (WP1) - Increased efficiency and sustainability
[20:20 Wheat] Maximising yield potential of wheat
20:20 Wheat [ISPG]
Publisher's version
Output statusPublished
Publication dates
Online21 Nov 2017
Publication process dates
Accepted11 Nov 2017
PublisherBiomed Central Ltd
Copyright licenseCC BY
ISSN1746-4811

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