Automated method to determine two critical growth stages of wheat: heading and flowering

A - Papers appearing in refereed journals

Sadeghi-Tehran, P., Sabermanesh, K., Virlet, N. and Hawkesford, M. J. 2017. Automated method to determine two critical growth stages of wheat: heading and flowering. Frontiers in Plant Science. 8 (252).

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

Recording growth stage information is an important aspect of precision agriculture, crop breeding and phenotyping. In practice, crop growth stage is still primarily monitored by-eye, which is not only laborious and time-consuming, but also subjective and error-prone. The application of computer vision on digital images offers a high-throughput and non-invasive alternative to manual observations and its use in agriculture and high-throughput phenotyping is increasing. This paper presents an automated method to detect wheat heading and flowering stages, which uses the application of computer vision on digital images. The bag-of-visual-word technique is used to identify the growth stage during heading and flowering within digital images. Scale invariant feature transformation feature extraction technique is used for lower level feature extraction; subsequently, local linear constraint coding and spatial pyramid matching are developed in the mid-level representation stage. At the end, support vector machine classification is used to train and test the data samples. The method outperformed existing algorithms, having yielded 95.24, 97.79, 99.59% at early, medium and late stages of heading, respectively and 85.45% accuracy for flowering detection. The results also illustrate that the proposed method is robust enough to handle complex environmental changes (illumination, occlusion). Although the proposed method is applied only on identifying growth stage in wheat, there is potential for application to other crops and categorization concepts, such as disease classification.

Year of Publication2017
JournalFrontiers in Plant Science
Journal citation8 (252)
Digital Object Identifier (DOI)
Open accessPublished as ‘gold’ (paid) open access
FunderBiotechnology and Biological Sciences Research Council
Funder project or codeWheat
[20:20 Wheat] Maximising yield potential of wheat
Publisher's version
Output statusPublished
Publication dates
Online27 Feb 2017
Publication process dates
Accepted09 Feb 2017
Copyright licenseCC BY
PublisherFrontiers Media SA

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