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

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

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 acquir...

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Bibliographic Details
Main Authors: Pouria Sadeghi-Tehran, Nicolas Virlet, Kasra Sabermanesh, Malcolm J. Hawkesford
Format: Article
Language:English
Published: BMC 2017-11-01
Series:Plant Methods
Subjects:
Field phenotyping
Learning-based segmentation
Fractional cover
Field Scanalyzer
RGB images
Online Access:http://link.springer.com/article/10.1186/s13007-017-0253-8

Internet

http://link.springer.com/article/10.1186/s13007-017-0253-8

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