Fig 1.
Variation of stomatal traits in terms of size and density from four different plant species: The eudicots are (a) Arabidopsis thaliana and (b) Phaseolus vulgaris; The grasses are (c) Oryza sativa and (d) Triticum aestivum.
Image adapted from [8].
Fig 2.
Schematic representation of the proposed pipeline for stomata classification and detection.
The proposed approach comprises two main modules: (i) the stomata classification process, where a classification model based on machine learning is created and trained with features extracted from microscope images; and (ii) the stomata detection approach, combining a sliding window mechanism to separate a microscope image into sub-images and a stomata identification process using the model created in (i).
Fig 3.
Visual representation of the combination of feature extraction approaches employed and classification algorithms to identify stomata.
Based on a manual selection of microscope subimages representing stomata and errors, several image descriptors were employed (DAISY, Oriented Gradient Histogram—HOG, Haralick Texture Features, Local Binary Patterns—LBP, GIST and Deep Convolutional Neural Networks—DCNN) and used to produce features to be used as input to machine learning techniques (Support Vector Machine—SVM, Multilayer Perceptron—MLP, and Adaboost) for identifying stomata.
Fig 4.
Visual representation of the stoma detection process.
From the train and test subsets established according to a k-fold cross validation, a sliding window mechanism was used to go through the image and identify possible regions of pixels corresponding to the stomata.
Fig 5.
Examples of subimages/regions from the microscope images of maize cultivars corresponding to (a) stomata, and (b) non-stomata.
These regions were manually selected and labeled in this work.
Fig 6.
A subset of microscope images used in this work.
Each of these images corresponds to different maize cultivars, which show great variability in stoma appearance and configuration.
Fig 7.
Different types of noise present in the microscopic images: (a) the usage of cyanoacrylate glue can generate air bubbles; (b) the microscope might capture leaves residuals; (c) the leaves might bend and create grooves in the image; (d) degraded stomata due to biological factors; and (e) low image quality due to equipment limitations.
Table 1.
Mean accuracies of the classifiers trained with image descriptor features for the stomata classification task.
We tested DAISY, Oriented Gradient Histogram—HOG, Haralick Texture Features, Local Binary Patterns—LBP and GIST descriptors, combined with support vector machine, multilayer perceptron and Adaboost machine learning algorithms.
Table 2.
Mean accuracies of the experiments based on deep learning features obtained with the tested convolutional neural network architectures (DenseNet, IResNet, Inception, MobileNet, NasNet, and VGG16) for the stomata classification task.
Table 3.
Final effectiveness results obtained with the most promising strategy for stomata detection (Support Vector Machine—SVM combined with VGG16 convolutional neural network) based on a 5-fold cross-validation strategy.
The performance evaluation considered the number (#) of stomata detected in relation to the real amount.
Fig 8.
Heatmap representation of the system performance.
Based on the sliding window mechanism applied to the original microscope image (a), different regions were considered as containing stomata (b) and used as an image mask (c) for image segmentation (d).
Fig 9.
Examples of the stomata classification detection results, including (a) corrected labeled sub-images and (b) false positive results.