Dataset Information

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) 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.

SUBMITTER: Kwong QB

PROVIDER: S-EPMC8313554 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Automated stomata detection in oil palm with convolutional neural network.

Kwong Qi Bin QB Wong Yick Ching YC Lee Phei Ling PL Sahaini Muhammad Syafiq MS Kon Yee Thung YT Kulaveerasingam Harikrishna H Appleton David Ross DR

Scientific reports 20210726 1

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 m ...[more]

PMID: 34312480

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Dataset Information

Automated stomata detection in oil palm with convolutional neural network.

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Automated stomata detection in oil palm with convolutional neural network.

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