Abstract
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. R2 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.
Highlights
Stomata are formed by pairs of specialized epidermal guard cells, which are the main pathways for gas exchange in the essential physiological processes of leaf plants, such as carbon assimilation, respiration, and transpiration (Kim et al, 2010)
We developed a fully automatic solution for stomatal index measurement that mainly consisted of two parts, namely, stomata and epidermal cell counting (Figure 2)
Performance Evaluations We evaluated the performance of the stomata detection algorithm using average precision (AP), which is defined as the area under an interpolated precision-recall curve
Summary
Stomata are formed by pairs of specialized epidermal guard cells, which are the main pathways for gas exchange in the essential physiological processes of leaf plants, such as carbon assimilation, respiration, and transpiration (Kim et al, 2010). The stomatal density and size are good indicators that reflect the response of plants to abiotic stresses in the environment and permit quantitative estimation of the stomatal gas exchange parameters (Sack and Buckley, 2016) These traits will alter with the growth of plants or the environment that cannot be used to reveal the stomata initiation and epidermal development across plant genotypes or species. The stomatal index, estimated as the number of stomata per number of epidermal cells plus stomata, is relatively constant during plant growth (Salisbury, 1928) It is of greater significance in studying the epidermal development process in plant physiology and their genetic basis in plant breeding for productivity (Royer, 2001; Sack and Buckley, 2016)
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