Automated stomata detection in oil palm with convolutional neural network
Abstract 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)...
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Nature Portfolio
2021
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oai:doaj.org-article:36a9aff3402a4aef91a2438fd42fbc762021-12-02T18:47:00ZAutomated stomata detection in oil palm with convolutional neural network10.1038/s41598-021-94705-42045-2322https://doaj.org/article/36a9aff3402a4aef91a2438fd42fbc762021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94705-4https://doaj.org/toc/2045-2322Abstract 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.Qi Bin KwongYick Ching WongPhei Ling LeeMuhammad Syafiq SahainiYee Thung KonHarikrishna KulaveerasingamDavid Ross AppletonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021) |
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Medicine R Science Q |
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Medicine R Science Q Qi Bin Kwong Yick Ching Wong Phei Ling Lee Muhammad Syafiq Sahaini Yee Thung Kon Harikrishna Kulaveerasingam David Ross Appleton Automated stomata detection in oil palm with convolutional neural network |
| description |
Abstract 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 |
article |
| author |
Qi Bin Kwong Yick Ching Wong Phei Ling Lee Muhammad Syafiq Sahaini Yee Thung Kon Harikrishna Kulaveerasingam David Ross Appleton |
| author_facet |
Qi Bin Kwong Yick Ching Wong Phei Ling Lee Muhammad Syafiq Sahaini Yee Thung Kon Harikrishna Kulaveerasingam David Ross Appleton |
| author_sort |
Qi Bin Kwong |
| title |
Automated stomata detection in oil palm with convolutional neural network |
| title_short |
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_sort |
automated stomata detection in oil palm with convolutional neural network |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/36a9aff3402a4aef91a2438fd42fbc76 |
| work_keys_str_mv |
AT qibinkwong automatedstomatadetectioninoilpalmwithconvolutionalneuralnetwork AT yickchingwong automatedstomatadetectioninoilpalmwithconvolutionalneuralnetwork AT pheilinglee automatedstomatadetectioninoilpalmwithconvolutionalneuralnetwork AT muhammadsyafiqsahaini automatedstomatadetectioninoilpalmwithconvolutionalneuralnetwork AT yeethungkon automatedstomatadetectioninoilpalmwithconvolutionalneuralnetwork AT harikrishnakulaveerasingam automatedstomatadetectioninoilpalmwithconvolutionalneuralnetwork AT davidrossappleton automatedstomatadetectioninoilpalmwithconvolutionalneuralnetwork |
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