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Automated Method to Determine Two Critical Growth Stages of Wheat: Heading and Flowering
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 visi...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5326764/ https://www.ncbi.nlm.nih.gov/pubmed/28289423 http://dx.doi.org/10.3389/fpls.2017.00252 |
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author | Sadeghi-Tehran, Pouria Sabermanesh, Kasra Virlet, Nicolas Hawkesford, Malcolm J. |
author_facet | Sadeghi-Tehran, Pouria Sabermanesh, Kasra Virlet, Nicolas Hawkesford, Malcolm J. |
author_sort | Sadeghi-Tehran, Pouria |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5326764 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-53267642017-03-13 Automated Method to Determine Two Critical Growth Stages of Wheat: Heading and Flowering Sadeghi-Tehran, Pouria Sabermanesh, Kasra Virlet, Nicolas Hawkesford, Malcolm J. Front Plant Sci Plant Science 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. Frontiers Media S.A. 2017-02-27 /pmc/articles/PMC5326764/ /pubmed/28289423 http://dx.doi.org/10.3389/fpls.2017.00252 Text en Copyright © 2017 Sadeghi-Tehran, Sabermanesh, Virlet and Hawkesford. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Sadeghi-Tehran, Pouria Sabermanesh, Kasra Virlet, Nicolas Hawkesford, Malcolm J. Automated Method to Determine Two Critical Growth Stages of Wheat: Heading and Flowering |
title | Automated Method to Determine Two Critical Growth Stages of Wheat: Heading and Flowering |
title_full | Automated Method to Determine Two Critical Growth Stages of Wheat: Heading and Flowering |
title_fullStr | Automated Method to Determine Two Critical Growth Stages of Wheat: Heading and Flowering |
title_full_unstemmed | Automated Method to Determine Two Critical Growth Stages of Wheat: Heading and Flowering |
title_short | Automated Method to Determine Two Critical Growth Stages of Wheat: Heading and Flowering |
title_sort | automated method to determine two critical growth stages of wheat: heading and flowering |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5326764/ https://www.ncbi.nlm.nih.gov/pubmed/28289423 http://dx.doi.org/10.3389/fpls.2017.00252 |
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