From Leaf to Label: A robust automated workflow for stomata detection

Autor: Francis wyffels, Sofie Meeus, Jan Van den Bulcke
Jazyk: angličtina
Rok vydání: 2019
Předmět:
0106 biological sciences
Computer science
Pipeline (computing)
stomata
detection
computer.software_genre
01 natural sciences
Convolutional neural network
FAMILIES
optical microscope images
herbarium
Transpiration
Meise Botanic Garden
0303 health sciences
Ecology
plants
General Medicine
CONVOLUTIONAL NEURAL-NETWORKS
deep neural networks
Original Article
Data mining
stomatal density
VGG19
TRAITS
PROTOCOLS
Technology and Engineering
Image processing
010603 evolutionary biology
GENOME SIZE
03 medical and health sciences
HANDBOOK
lcsh:QH540-549.5
Ecology
Evolution
Behavior and Systematics
030304 developmental biology
Nature and Landscape Conservation
business.industry
Deep learning
fungi
Biology and Life Sciences
deep learning
Pattern recognition
Herbaria
IMPRESSIONS
STANDARDIZED MEASUREMENT
Workflow
Test set
DENSITY
Artificial intelligence
lcsh:Ecology
Precision and recall
business
computer
Zdroj: Biodiversity Information Science and Standards 3: e37504
BIODIVERSITY INFORMATION SCIENCE AND STANDARDS
Ecology and Evolution, Vol 10, Iss 17, Pp 9178-9191 (2020)
ECOLOGY AND EVOLUTION
Ecology and Evolution
ISSN: 2535-0897
2045-7758
Popis: Plant leaf stomata are the gatekeepers of the atmosphere–plant interface and are essential building blocks of land surface models as they control transpiration and photosynthesis. Although more stomatal trait data are needed to significantly reduce the error in these model predictions, recording these traits is time‐consuming, and no standardized protocol is currently available. Some attempts were made to automate stomatal detection from photomicrographs; however, these approaches have the disadvantage of using classic image processing or targeting a narrow taxonomic entity which makes these technologies less robust and generalizable to other plant species. We propose an easy‐to‐use and adaptable workflow from leaf to label. A methodology for automatic stomata detection was developed using deep neural networks according to the state of the art and its applicability demonstrated across the phylogeny of the angiosperms.We used a patch‐based approach for training/tuning three different deep learning architectures. For training, we used 431 micrographs taken from leaf prints made according to the nail polish method from herbarium specimens of 19 species. The best‐performing architecture was tested on 595 images of 16 additional species spread across the angiosperm phylogeny.The nail polish method was successfully applied in 78% of the species sampled here. The VGG19 architecture slightly outperformed the basic shallow and deep architectures, with a confidence threshold equal to 0.7 resulting in an optimal trade‐off between precision and recall. Applying this threshold, the VGG19 architecture obtained an average F‐score of 0.87, 0.89, and 0.67 on the training, validation, and unseen test set, respectively. The average accuracy was very high (94%) for computed stomatal counts on unseen images of species used for training.The leaf‐to‐label pipeline is an easy‐to‐use workflow for researchers of different areas of expertise interested in detecting stomata more efficiently. The described methodology was based on multiple species and well‐established methods so that it can serve as a reference for future work.
Our paper proposes a newly developed methodology for automatic stomata detection using deep neural networks according to the state of the art and demonstrates its applicability across the phylogeny of the angiosperms.
Databáze: OpenAIRE
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