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

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.

GinJinn2: Object detection and segmentation for ecology and evolution

“…The stomata analysis serves as a basic example of instance segmentation. Despite several previous works on the automated examination of stomata (Toda et al, 2018;Fetter et al, 2019;Li et al, 2019;Carrasco et al, 2020;Casado-García et al, 2020;Meeus et al, 2020;Song et al, 2020), this contribution, to our knowledge, is the first trying to automatically segment whole stomata (represented by their guard cells) With the presented exemplary analyses, we hope to provide guidance for the application of GinJinn2 for automatic data collection and feature extraction. Despite GinJinn2's progress compared to its predecessor, there is still room for further improvements.…”

An Integrated Method for Tracking and Monitoring Stomata Dynamics from Microscope Videos

“…Li et al used a faster region-based convolutional neural network (Faster R-CNN) to segment single stoma from different plant images and then measured these single stomatal morphological features with a manual CV model [17]. Song et al obtained the contour coordinates of the pore regions in microscope images of leaves through Mask R-CNN (region-based convolutional neural network), which is an instance segmentation model [18]. Although these methods can obtain stomatal morphological features automatically or semiautomatically, detection dataset in these methods is static images, which cannot reflect dynamic changes of stomata.…”

Optimizing the Experimental Method for Stomata-Profiling Automation of Soybean Leaves Based on Deep Learning

“…Even though several preparation methodologies were covered, their predictive model is not currently generalized to detect stomata from microscopic images generated by other methods. An automatic segmentation strategy was also used to detect and measure stomatal pores [22]. Similarly, the predictive model may overfit the training image styles, which may differ from other microscopic images.…”