Machine learning methods for automatic segmentation of images of field-and glasshouse-based plants for high-throughput phenotyping

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dc.contributor.authorOkyere, Frank Gyan
dc.contributor.authorCudjoe, Daniel M.
dc.contributor.authorSadeghi-Tehran, Pouria
dc.contributor.authorVirlet, Nicolas
dc.contributor.authorRiche, Andrew B.
dc.contributor.authorCastle, March
dc.contributor.authorGreche, Latifa
dc.contributor.authorMohare, Fady
dc.contributor.authorSimms, Daniel M.
dc.contributor.authorMhada, Manal
dc.contributor.authorHawkesford, Malcolm John
dc.date.accessioned2023-06-23T08:43:21Z
dc.date.available2023-06-23T08:43:21Z
dc.date.issued2023-05-19
dc.description.abstractImage segmentation is a fundamental but critical step for achieving automated high- throughput phenotyping. While conventional segmentation methods perform well in homogenous environments, the performance decreases when used in more complex environments. This study aimed to develop a fast and robust neural-network-based segmentation tool to phenotype plants in both field and glasshouse environments in a high-throughput manner. Digital images of cowpea (from glasshouse) and wheat (from field) with different nutrient supplies across their full growth cycle were acquired. Image patches from 20 randomly selected images from the acquired dataset were transformed from their original RGB format to multiple color spaces. The pixels in the patches were annotated as foreground and background with a pixel having a feature vector of 24 color properties. A feature selection technique was applied to choose the sensitive features, which were used to train a multilayer perceptron network (MLP) and two other traditional machine learning models: support vector machines (SVMs) and random forest (RF). The performance of these models, together with two standard color-index segmentation techniques (excess green (ExG) and excess green–red (ExGR)), was compared. The proposed method outperformed the other methods in producing quality segmented images with over 98%-pixel classification accuracy. Regression models developed from the different segmentation methods to predict Soil Plant Analysis Development (SPAD) values of cowpea and wheat showed that images from the proposed MLP method produced models with high predictive power and accuracy comparably. This method will be an essential tool for the development of a data analysis pipeline for high-throughput plant phenotyping. The proposed technique is capable of learning from different environmental conditions, with a high level of robustness.en_UK
dc.identifier.citationOkyere FG, Cudjoe D, Sadeghi-Tehran P,et al., (2023) Machine learning methods for automatic segmentation of images of field-and glasshouse-based plants for high-throughput phenotyping. Plants, Volume 12, Issue 10, May 2023, Article number 2035en_UK
dc.identifier.issn2223-7747
dc.identifier.urihttps://doi.org/10.3390/plants12102035
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19876
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectfeature extractionen_UK
dc.subjectimagingen_UK
dc.subjectmachine learningen_UK
dc.subjectphenotypingen_UK
dc.subjectsegmentationen_UK
dc.titleMachine learning methods for automatic segmentation of images of field-and glasshouse-based plants for high-throughput phenotypingen_UK
dc.typeArticleen_UK

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