Dataset Information

Automatic cell identification and counting of leaf epidermis for plant phenotyping.

ABSTRACT: The counting of leaf epidermal cells is useful to study the plant developmental changes produced by environmental or genetic factors. The scanning electron microscopy can be used, but it is expensive and time-consuming. Methods using optical microscopy are also available, but they still require leaves pre-treatment and manual cell identification. We propose a quick and simple method for counting leaf epidermal cells without leaf treatments and based on automated cell identification and marking. It allows to highly improve the representativeness of leaf epidermis screening, aiming at a high-throughput plant phenotyping approach.•The leaves are pressed between two glass slides without any pre-treatment and digital micrographs are acquired under incident light.•Epidermal cells are automatically identified and counted by means of a "macro" of ImageJ•The cell count obtained applying the procedure of image processing is very close to that obtainable by manual cell identification.

SUBMITTER: Mele G

PROVIDER: S-EPMC7132086 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Automatic cell identification and counting of leaf epidermis for plant phenotyping.

Mele Giacomo G Gargiulo Laura L

MethodsX 20200314

The counting of leaf epidermal cells is useful to study the plant developmental changes produced by environmental or genetic factors. The scanning electron microscopy can be used, but it is expensive and time-consuming. Methods using optical microscopy are also available, but they still require leaves pre-treatment and manual cell identification. We propose a quick and simple method for counting leaf epidermal cells without leaf treatments and based on automated cell identification and marking. ...[more]

PMID: 32274336

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Project description:The stomatal index of the leaf is the ratio of the number of stomata to the total number of stomata and epidermal cells. Comparing with the stomatal density, the stomatal index is relatively constant in environmental conditions and the age of the leaf and, therefore, of diagnostic characteristics for a given genotype or species. Traditional assessment methods involve manual counting of the number of stomata and epidermal cells in microphotographs, which is labor-intensive and time-consuming. Although several automatic measurement algorithms of stomatal density have been proposed, no stomatal index pipelines are currently available. The main aim of this research is to develop an automated stomatal index measurement pipeline. The proposed method employed Faster regions with convolutional neural networks (R-CNN) and U-Net and image-processing techniques to count stomata and epidermal cells, and subsequently calculate the stomatal index. To improve the labeling speed, a semi-automatic strategy was employed for epidermal cell annotation in each micrograph. Benchmarking the pipeline on 1,000 microscopic images of leaf epidermis in the wheat dataset (Triticum aestivum L.), the average counting accuracies of 98.03 and 95.03% for stomata and epidermal cells, respectively, and the final measurement accuracy of the stomatal index of 95.35% was achieved. R 2 values between automatic and manual measurement of stomata, epidermal cells, and stomatal index were 0.995, 0.983, and 0.895, respectively. The average running time (ART) for the entire pipeline could be as short as 0.32 s per microphotograph. The proposed pipeline also achieved a good transferability on the other families of the plant using transfer learning, with the mean counting accuracies of 94.36 and 91.13% for stomata and epidermal cells and the stomatal index accuracy of 89.38% in seven families of the plant. The pipeline is an automatic, rapid, and accurate tool for the stomatal index measurement, enabling high-throughput phenotyping, and facilitating further understanding of the stomatal and epidermal development for the plant physiology community. To the best of our knowledge, this is the first deep learning-based microphotograph analysis pipeline for stomatal index assessment.

Dataset Information

Automatic cell identification and counting of leaf epidermis for plant phenotyping.

Publications

Automatic cell identification and counting of leaf epidermis for plant phenotyping.

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