Cargando…
Free and open-source software for object detection, size, and colour determination for use in plant phenotyping
BACKGROUND: Object detection, size determination, and colour detection of images are tools commonly used in plant science. Key examples of this include identification of ripening stages of fruit such as tomatoes and the determination of chlorophyll content as an indicator of plant health. While meth...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10647133/ https://www.ncbi.nlm.nih.gov/pubmed/37964366 http://dx.doi.org/10.1186/s13007-023-01103-0 |
_version_ | 1785147508192706560 |
---|---|
author | Wright, Harry Charles Lawrence, Frederick Antonio Ryan, Anthony John Cameron, Duncan Drummond |
author_facet | Wright, Harry Charles Lawrence, Frederick Antonio Ryan, Anthony John Cameron, Duncan Drummond |
author_sort | Wright, Harry Charles |
collection | PubMed |
description | BACKGROUND: Object detection, size determination, and colour detection of images are tools commonly used in plant science. Key examples of this include identification of ripening stages of fruit such as tomatoes and the determination of chlorophyll content as an indicator of plant health. While methods exist for determining these important phenotypes, they often require proprietary software or require coding knowledge to adapt existing code. RESULTS: We provide a set of free and open-source Python scripts that, without any adaptation, are able to perform background correction and colour correction on images using a ColourChecker chart. Further scripts identify objects, use an object of known size to calibrate for size, and extract the average colour of objects in RGB, Lab, and YUV colour spaces. We use two examples to demonstrate the use of these scripts. We show the consistency of these scripts by imaging in four different lighting conditions, and then we use two examples to show how the scripts can be used. In the first example, we estimate the lycopene content in tomatoes (Solanum lycopersicum) var. Tiny Tim using fruit images and an exponential model to predict lycopene content. We demonstrate that three different cameras (a DSLR camera and two separate mobile phones) are all able to model lycopene content. The models that predict lycopene or chlorophyll need to be adjusted depending on the camera used. In the second example, we estimate the chlorophyll content of basil (Ocimum basilicum) using leaf images and an exponential model to predict chlorophyll content. CONCLUSION: A fast, cheap, non-destructive, and inexpensive method is provided for the determination of the size and colour of plant materials using a rig consisting of a lightbox, camera, and colour checker card and using free and open-source scripts that run in Python 3.8. This method accurately predicted the lycopene content in tomato fruit and the chlorophyll content in basil leaves. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13007-023-01103-0. |
format | Online Article Text |
id | pubmed-10647133 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106471332023-11-15 Free and open-source software for object detection, size, and colour determination for use in plant phenotyping Wright, Harry Charles Lawrence, Frederick Antonio Ryan, Anthony John Cameron, Duncan Drummond Plant Methods Methodology BACKGROUND: Object detection, size determination, and colour detection of images are tools commonly used in plant science. Key examples of this include identification of ripening stages of fruit such as tomatoes and the determination of chlorophyll content as an indicator of plant health. While methods exist for determining these important phenotypes, they often require proprietary software or require coding knowledge to adapt existing code. RESULTS: We provide a set of free and open-source Python scripts that, without any adaptation, are able to perform background correction and colour correction on images using a ColourChecker chart. Further scripts identify objects, use an object of known size to calibrate for size, and extract the average colour of objects in RGB, Lab, and YUV colour spaces. We use two examples to demonstrate the use of these scripts. We show the consistency of these scripts by imaging in four different lighting conditions, and then we use two examples to show how the scripts can be used. In the first example, we estimate the lycopene content in tomatoes (Solanum lycopersicum) var. Tiny Tim using fruit images and an exponential model to predict lycopene content. We demonstrate that three different cameras (a DSLR camera and two separate mobile phones) are all able to model lycopene content. The models that predict lycopene or chlorophyll need to be adjusted depending on the camera used. In the second example, we estimate the chlorophyll content of basil (Ocimum basilicum) using leaf images and an exponential model to predict chlorophyll content. CONCLUSION: A fast, cheap, non-destructive, and inexpensive method is provided for the determination of the size and colour of plant materials using a rig consisting of a lightbox, camera, and colour checker card and using free and open-source scripts that run in Python 3.8. This method accurately predicted the lycopene content in tomato fruit and the chlorophyll content in basil leaves. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13007-023-01103-0. BioMed Central 2023-11-15 /pmc/articles/PMC10647133/ /pubmed/37964366 http://dx.doi.org/10.1186/s13007-023-01103-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Methodology Wright, Harry Charles Lawrence, Frederick Antonio Ryan, Anthony John Cameron, Duncan Drummond Free and open-source software for object detection, size, and colour determination for use in plant phenotyping |
title | Free and open-source software for object detection, size, and colour determination for use in plant phenotyping |
title_full | Free and open-source software for object detection, size, and colour determination for use in plant phenotyping |
title_fullStr | Free and open-source software for object detection, size, and colour determination for use in plant phenotyping |
title_full_unstemmed | Free and open-source software for object detection, size, and colour determination for use in plant phenotyping |
title_short | Free and open-source software for object detection, size, and colour determination for use in plant phenotyping |
title_sort | free and open-source software for object detection, size, and colour determination for use in plant phenotyping |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10647133/ https://www.ncbi.nlm.nih.gov/pubmed/37964366 http://dx.doi.org/10.1186/s13007-023-01103-0 |
work_keys_str_mv | AT wrightharrycharles freeandopensourcesoftwareforobjectdetectionsizeandcolourdeterminationforuseinplantphenotyping AT lawrencefrederickantonio freeandopensourcesoftwareforobjectdetectionsizeandcolourdeterminationforuseinplantphenotyping AT ryananthonyjohn freeandopensourcesoftwareforobjectdetectionsizeandcolourdeterminationforuseinplantphenotyping AT cameronduncandrummond freeandopensourcesoftwareforobjectdetectionsizeandcolourdeterminationforuseinplantphenotyping |