Cargando…

Automated stomata detection in oil palm with convolutional neural network

Stomatal density is an important trait for breeding selection of drought tolerant oil palms; however, its measurement is extremely tedious. To accelerate this process, we developed an automated system. Leaf samples from 128 palms ranging from nursery (1 years old), juvenile (2–3 years old) and matur...

Descripción completa

Detalles Bibliográficos
Autores principales: Kwong, Qi Bin, Wong, Yick Ching, Lee, Phei Ling, Sahaini, Muhammad Syafiq, Kon, Yee Thung, Kulaveerasingam, Harikrishna, Appleton, David Ross
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8313554/
https://www.ncbi.nlm.nih.gov/pubmed/34312480
http://dx.doi.org/10.1038/s41598-021-94705-4
_version_ 1783729374014472192
author Kwong, Qi Bin
Wong, Yick Ching
Lee, Phei Ling
Sahaini, Muhammad Syafiq
Kon, Yee Thung
Kulaveerasingam, Harikrishna
Appleton, David Ross
author_facet Kwong, Qi Bin
Wong, Yick Ching
Lee, Phei Ling
Sahaini, Muhammad Syafiq
Kon, Yee Thung
Kulaveerasingam, Harikrishna
Appleton, David Ross
author_sort Kwong, Qi Bin
collection PubMed
description Stomatal density is an important trait for breeding selection of drought tolerant oil palms; however, its measurement is extremely tedious. To accelerate this process, we developed an automated system. Leaf samples from 128 palms ranging from nursery (1 years old), juvenile (2–3 years old) and mature (> 10 years old) were collected to build an oil palm specific stomata detection model. Micrographs were split into tiles, then used to train a stomata object detection convolutional neural network model through transfer learning. The detection model was then tested on leaf samples acquired from three independent oil palm populations of young seedlings (A), juveniles (B) and productive adults (C). The detection accuracy, measured in precision and recall, was 98.00% and 99.50% for set A, 99.70% and 97.65% for set B, and 99.55% and 99.62% for set C, respectively. The detection model was cross-applied to another set of adult palms using stomata images taken with a different microscope and under different conditions (D), resulting in precision and recall accuracy of 99.72% and 96.88%, respectively. This indicates that the model built generalized well, in addition has high transferability. With the completion of this detection model, stomatal density measurement can be accelerated. This in turn will accelerate the breeding selection for drought tolerance.
format Online
Article
Text
id pubmed-8313554
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-83135542021-07-27 Automated stomata detection in oil palm with convolutional neural network Kwong, Qi Bin Wong, Yick Ching Lee, Phei Ling Sahaini, Muhammad Syafiq Kon, Yee Thung Kulaveerasingam, Harikrishna Appleton, David Ross Sci Rep Article Stomatal density is an important trait for breeding selection of drought tolerant oil palms; however, its measurement is extremely tedious. To accelerate this process, we developed an automated system. Leaf samples from 128 palms ranging from nursery (1 years old), juvenile (2–3 years old) and mature (> 10 years old) were collected to build an oil palm specific stomata detection model. Micrographs were split into tiles, then used to train a stomata object detection convolutional neural network model through transfer learning. The detection model was then tested on leaf samples acquired from three independent oil palm populations of young seedlings (A), juveniles (B) and productive adults (C). The detection accuracy, measured in precision and recall, was 98.00% and 99.50% for set A, 99.70% and 97.65% for set B, and 99.55% and 99.62% for set C, respectively. The detection model was cross-applied to another set of adult palms using stomata images taken with a different microscope and under different conditions (D), resulting in precision and recall accuracy of 99.72% and 96.88%, respectively. This indicates that the model built generalized well, in addition has high transferability. With the completion of this detection model, stomatal density measurement can be accelerated. This in turn will accelerate the breeding selection for drought tolerance. Nature Publishing Group UK 2021-07-26 /pmc/articles/PMC8313554/ /pubmed/34312480 http://dx.doi.org/10.1038/s41598-021-94705-4 Text en © The Author(s) 2021 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/) .
spellingShingle Article
Kwong, Qi Bin
Wong, Yick Ching
Lee, Phei Ling
Sahaini, Muhammad Syafiq
Kon, Yee Thung
Kulaveerasingam, Harikrishna
Appleton, David Ross
Automated stomata detection in oil palm with convolutional neural network
title Automated stomata detection in oil palm with convolutional neural network
title_full Automated stomata detection in oil palm with convolutional neural network
title_fullStr Automated stomata detection in oil palm with convolutional neural network
title_full_unstemmed Automated stomata detection in oil palm with convolutional neural network
title_short Automated stomata detection in oil palm with convolutional neural network
title_sort automated stomata detection in oil palm with convolutional neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8313554/
https://www.ncbi.nlm.nih.gov/pubmed/34312480
http://dx.doi.org/10.1038/s41598-021-94705-4
work_keys_str_mv AT kwongqibin automatedstomatadetectioninoilpalmwithconvolutionalneuralnetwork
AT wongyickching automatedstomatadetectioninoilpalmwithconvolutionalneuralnetwork
AT leepheiling automatedstomatadetectioninoilpalmwithconvolutionalneuralnetwork
AT sahainimuhammadsyafiq automatedstomatadetectioninoilpalmwithconvolutionalneuralnetwork
AT konyeethung automatedstomatadetectioninoilpalmwithconvolutionalneuralnetwork
AT kulaveerasingamharikrishna automatedstomatadetectioninoilpalmwithconvolutionalneuralnetwork
AT appletondavidross automatedstomatadetectioninoilpalmwithconvolutionalneuralnetwork