Rapid Estimation of Stomatal Density and Stomatal Area of Plant Leaves Based on Object-Oriented Classification and Its Ecological Trade-Off Strategy Analysis

Zhu, Jiyou; Yu, Qiang; Xu, Chengyang; JinHang, Li; Qin, Guoming

doi:10.3390/f9100616

Cited by 27 publications

(27 citation statements)

References 58 publications

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“…A total of 900 stomatal microscopic images were collected in this experiment. Finally, the stomatal density (number•cm − 2 ), stomatal size (μm 2 ), and stomatal aperture (μm) were further calculated according to the method of Zhu et al [56]. Sixty healthy, fully-expanded leaves were randomly selected from each tree, placed them in a 5°C icebox, and immediately brought them back to the laboratory to complete the determination of leaf functional traits within 1 h. The collected fresh leaves were measured for leaf thickness by upper, middle and lower portions using an electronic vernier caliper.…”

Section: Determination Of Leaf Functional Traitsmentioning

confidence: 99%

Effect of simulated warming on leaf functional traits of urban greening plants

Zhu

Cao

et al. 2020

BMC Plant Biol

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Background: Response and adaptation strategies of plants to the environment have always been the core issues in ecological research. So far, relatively little study exists on its functional traits responses to warming, especially in an urban environment. This information is the key to help understand plant responses and trade-off strategy to urban warming. Results: We chose the common greening trees of mature age in Beijing (Fraxinus pennsylvanica, Koelreuteria paniculata, and Sophora japonica) as the research subjects, and used infrared heaters to simulate warming for three gradients of natural temperature (CK), moderate warming (T1) and severe warming (T2). Results showed that:(1) Leaf dry matter content (LDMC), chlorophyll content (CHL), leaf tissue density (LTD), and stomatal density (SD) all increased with temperature warming. Specific leaf area (SLA), stomatal size (SS), and stomatal aperture (SA) decreased with simulated warming. (2) SLA was extremely significantly negatively correlated with CHL, LDMC, LTD and SD (P < 0.01), and was extremely significantly positively correlated with SS (P < 0.01). SA was extremely negatively correlated with SD (P < 0.01), and was extremely significantly positively correlated with SS (P < 0.01). There was a significant positive correlation between LDMC and LTD (P < 0.01). This showed that urban greening trees adapted to the environment by coordinating adjustment among leaf functional traits. (3) Under the T1 treatment, the R 2 and slope among the leaf traits were higher than CK, and the significance was also enhanced. The correlation between leaf traits was strengthened in this warming environment. Conversely, it will weaken the correlation between leaf traits under the T2 treatment. Conclusion: Our study demonstrated that there was a strong trade-off between leaf functional traits in the urban warming environment. Plants in the warming environment have adopted relatively consistent trade-offs and adaptation strategies. Moderate warming was more conducive to strengthening their trade-off potential. It is further verified that the global leaf economics spectrum also exists in urban ecosystems, which is generally tend to a quick-investment return type with the characteristics of thick leaves, strong photosynthetic capacity, low transpiration efficiency and long life in urban environments.

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Section: Determination Of Leaf Functional Traitsmentioning

confidence: 99%

Effect of simulated warming on leaf functional traits of urban greening plants

Zhu

Cao

et al. 2020

BMC Plant Biol

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“…The size of open stomata and stomatal density were measured according to Jiyou Zhu [ 54 ]. Using multiscale segmentation and classification recognition as well as microscopy images of leaf stomata using a computer software (eCognition Development 64, Munich, Germany) with such, segmentation parameters as scale parameter 120, shape parameter 0.5, and compactness parameter 0.8.…”

Section: Methodsmentioning

confidence: 99%

Effect of Iron Source and Medium pH on Growth and Development of Sorbus commixta In Vitro

Xiao

Park

Guo

et al. 2020

IJMS

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Sorbus commixta is a valuable hardwood plant with a high economical value for its medicinal and ornamental qualities. The aim of this work was to investigate the effects of the iron (Fe) source and medium pH on the growth and development of S. commixta in vitro. The Fe sources used, including non-chelated iron sulfate (FeSO4), iron ethylenediaminetetraacetic acid (Fe-EDTA), and iron diethylenetriaminepentaacetic acid (Fe-DTPA), were supplemented to the Multipurpose medium with a final Fe concentration of 2.78 mg·L−1. The medium without any supplementary Fe was used as the control. The pH of the agar-solidified medium was adjusted to either 4.70, 5.70, or 6.70. The experiment was conducted in a culture room for six weeks with 25 °C day and night temperatures, and a 16-h photoperiod with a light intensity of 50 mmol·m−2·s−1 photosynthetic photon flux density (PPFD). Both the Fe source and pH affected the growth and development of the micropropagated plants in vitro. The leaves were greener in the pH 4.70 and 5.70 treatments. The tissue Fe content decreased with the increase of the medium pH. The leaf chlorophyll content was similar between plants treated with FeSO4 and those with Fe-EDTA. The numbers of the shoots and roots of plantlets treated with FeSO4 were 2.5 and 2 times greater than those of the control, respectively. The fresh and dry weights of the shoot and the root were the greatest for plants treated with Fe-EDTA combined with pH 5.70. The calcium, magnesium, and manganese contents in the plantlets increased in the pH 5.70 treatments regardless of the Fe source. Supplementary Fe decreased the activity of ferric chelate reductase. Overall, although the plantlets absorbed more Fe at pH 4.70, Fe-EDTA combined with pH 5.70 was found to be the best for the growth and development of S. commixta in vitro.

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“…The pore area represents the size of the channel for gas and moisture exchange [9], and it determines the stomatal conductance to CO 2 and H 2 O [17], which is defined as the product of the pore length (2a), pore width (2b), and pi. The pore area is:…”

Section: Stomatal Pore Measurementmentioning

confidence: 99%

“…The rest of the epidermis is covered by an impervious cuticle, with limited possibilities for gas exchange [5]. The gas exchange capacity depends on stomatal density (i.e., stomatal number per unit area), stomatal size (length and width), and pore dimensions (length and aperture) [6][7][8][9]. Pore area is dynamically adjusted by changes in pore aperture, since pore length is rather rigid during the opening and closure of stomata [10,11].…”

Section: Introductionmentioning

confidence: 99%

An Automatic Method for Stomatal Pore Detection and Measurement in Microscope Images of Plant Leaf Based on a Convolutional Neural Network Model

Song

et al. 2020

Forests

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Stomata are microscopic pores on the plant epidermis that regulate the water content and CO2 levels in leaves. Thus, they play an important role in plant growth and development. Currently, most of the common methods for the measurement of pore anatomy parameters involve manual measurement or semi-automatic analysis technology, which makes it difficult to achieve high-throughput and automated processing. This paper presents a method for the automatic segmentation and parameter calculation of stomatal pores in microscope images of plant leaves based on deep convolutional neural networks. The proposed method uses a type of convolutional neural network model (Mask R-CNN (region-based convolutional neural network)) to obtain the contour coordinates of the pore regions in microscope images of leaves. The anatomy parameters of pores are then obtained by ellipse fitting technology, and the quantitative analysis of pore parameters is implemented. Stomatal microscope image datasets for black poplar leaves were obtained using a large depth-of-field microscope observation system, the VHX-2000, from Keyence Corporation. The images used in the training, validation, and test sets were taken randomly from the datasets (562, 188, and 188 images, respectively). After 10-fold cross validation, the 188 test images were found to contain an average of 2278 pores (pore widths smaller than 0.34 μm (1.65 pixels) were considered to be closed stomata), and an average of 2201 pores were detected by our network with a detection accuracy of 96.6%, and the intersection of union (IoU) of the pores was 0.82. The segmentation results of 2201 stomatal pores of black poplar leaves showed that the average measurement accuracies of the (a) pore length, (b) pore width, (c) area, (d) eccentricity, and (e) degree of stomatal opening, with a ratio of width-to-maximum length of a stomatal pore, were (a) 94.66%, (b) 93.54%, (c) 90.73%, (d) 99.09%, and (e) 92.95%, respectively. The proposed stomatal pore detection and measurement method based on the Mask R-CNN can automatically measure the anatomy parameters of pores in plants, thus helping researchers to obtain accurate stomatal pore information for leaves in an efficient and simple way.

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Rapid Estimation of Stomatal Density and Stomatal Area of Plant Leaves Based on Object-Oriented Classification and Its Ecological Trade-Off Strategy Analysis

Cited by 27 publications

References 58 publications

Effect of simulated warming on leaf functional traits of urban greening plants

Effect of simulated warming on leaf functional traits of urban greening plants

Effect of Iron Source and Medium pH on Growth and Development of Sorbus commixta In Vitro

An Automatic Method for Stomatal Pore Detection and Measurement in Microscope Images of Plant Leaf Based on a Convolutional Neural Network Model

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