Abstract
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.
Highlights
Stomata control the fluxes in carbon dioxide and water vapor levels across a leaf [1,2,3,4]
We propose an automatic high-throughput method based on the mask region-based convolutional neural network (Mask R-CNN) [31] to acquire parameters of stomatal pore anatomy
(2) trained use the network trained model to obtain the mask contour coordinates of each pore in the test image; network model to obtain the mask contour coordinates of each pore in the test and (3) obtain parameters of the pores on thebased least squares of ellipses according to the image; and (3)the obtain the parameters ofbased the pores on the fitting least squares fitting of ellipses contour coordinates of the mask and stomatal pore measurement model according to the contour coordinates of the mask and stomatal pore measurement model [34]
Summary
Stomata control the fluxes in carbon dioxide and water vapor levels across a leaf [1,2,3,4]. Pore length and stomatal density are anatomical features that are set during leaf elongation [10,16]. These stomatal features have been the focus of a wide range of studies [6,7,8,17,18]. They are often used to estimate stomatal conductance (gs) based on the equation by Brown and Escombe [19] or modified versions of this equation [17,20]. Estimations of gs from stomatal anatomy features are important when the effect of changing a single feature on gs is investigated [21,22]
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