StoManager1 is a high-throughput tool utilizing empirical and theoretical algorithms and convolutional neural networks to automatically detect, count, and measure over 30 stomatal and guard cell metrics, facilitating the automation of measuring leaf stomata.

Plant stomata are essential channels for gas exchange between plants and the environment... (truncated for brevity)

The traditional methods of analyzing stomatal traits are mostly manual observation and measurement. These methods are time-consuming, labor-intensive, and inefficient. Some methods have been proposed for the automatic recognition and counting of stomata, however most of those methods could not complete the automatic measurement of stomata parameters at the same time. Some non-deep learning methods could automatically measure the parameters of stomata, but they could not complete the automatic recognition and detection of stomata. In this paper, a deep learning-based method was proposed for automatically identifying, counting and measuring stomata of maize (Zea mays L.) leaves at the same time. An improved YOLO (You Only Look Once) deep learning model was proposed to identify stomata of maize leaves automatically, and an entropy rate superpixel algorithm was used for the accurate measurement of stomatal parameters. According to the characteristics of the stomata images data set, the network structure of YOLOv5 was modified, which greatly reduced the training time without affecting the recognition performance. The predictor in YOLO deep learning model was optimized, which reduced the false detection rate. At the same time, the 16-fold and 32-fold down-sampling layers were simplified according to the characteristics of stomatal objects, which improved the recognition efficiency. Experimental results showed that the recognition precision of the improved YOLO deep learning model reached 95.3% on the maize leaves stomatal data set, and the average accuracy of parameter measurement reached 90%. The proposed method could fully automatically complete the recognition, counting and measurement of stomata of plants, which can help agricultural scientists and botanists to conduct large-scale researches of stomatal morphology, structure and physiology, as well as the researches combined with genetic analysis or molecular-level analysis.

Stomata play important roles in gas and water exchange in leaves. The morphological features of stomata and pavement cells are highly plastic and are regulated during development. However, it is very laborious and time-consuming to collect accurate quantitative data from the leaf surface by manual phenotyping. Here, we introduce LeafNet, a tool that automatically localizes stomata, segments pavement cells (to prepare them for quantification), and reports multiple morphological parameters for a variety of leaf epidermal images, especially bright-field microscopy images. LeafNet employs a hierarchical strategy to identify stomata using a deep convolutional network and then segments pavement cells on stomata-masked images using a region merging method. LeafNet achieved promising performance on test images for quantifying different phenotypes of individual stomata and pavement cells compared with six currently available tools, including StomataCounter, Cellpose, PlantSeg, and PaCeQuant. LeafNet shows great flexibility, and we improved its ability to analyze bright-field images from a broad range of species as well as confocal images using transfer learning. Large-scale images of leaves can be efficiently processed in batch mode and interactively inspected with a graphic user interface or a web server (https://leafnet.whu.edu.cn/). The functionalities of LeafNet could easily be extended and will enhance the efficiency and productivity of leaf phenotyping for many plant biologists.

Stomata are the main medium of water exchange in plants, regulating gas exchange and responsible for the processes of photosynthesis and transpiration. Stomata are surrounded by guard cells and the transpiration rate is controlled by opening and closing stomata. Stomatal state (open and close) plays an important role in describing the health of plants. In addition, counting the number of stomata is of great signicance for scientists to study the number of opening and closeing stomata and to measure their density and distribution on the leaf surface through different sampling techniques. Although some techniques for calculating the number of stomata have been proposed, these methods are used to produce samples in isolation and then to identify and classify the states in the sample leaves. We improved YOLO-X and then implemented a transfer learning method to count the number of stomata and identify the stomatal opening and closing status of live black poplar leaves. In the end, the average accuracy and recall of the method were 98.3% and 95.9%, which helped researchers to obtain accurate information on leaf stomatal opening and closing status in an ecient and simple way.

Using microscopy to investigate stomatal behaviour is a common technique in plant physiology research. Manual inspection and measurement of stomatal features is a low throughput process in terms of time and human effort, which relies on expert knowledge to identify and measure stomata accurately. This process represents a significant bottleneck in research pipelines, adding significant researcher time to any project that requires it. To alleviate this, we introduce StomaAI (SAI): a reliable and user-friendly tool that measures stomata of the model plant Arabidopsis (dicot) and the crop plant barley (monocot grass) via the application of deep computer vision. We evaluated the reliability of predicted measurements: SAI is capable of producing measurements consistent with human experts and successfully reproduced conclusions of published datasets. Hence, SAI boosts the number of images that biologists can evaluate in a fraction of the time so is capable of obtaining more accurate and representative results.
### Competing Interest Statement
The authors have declared no competing interest.

To measure stomatal traits automatically and nondestructively, a new method for detecting stomata was proposed... (truncated for brevity)

Stomata are integral to plant performance, enabling the exchange of gases between the atmosphere and the plant... (truncated for brevity)

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.

Background: Stomata analysis using microscope imagery provides important insight into plant physiology, health and the surrounding environmental conditions. Plant scientists are now able to conduct automated high-throughput analysis of stomata in microscope data, however, existing detection methods are sensitive to the appearance of stomata in the training images, thereby limiting general applicability. In addition, existing methods only generate bounding-boxes around detected stomata, which require users to implement additional image processing steps to study stomata morphology. In this paper, we develop a fully automated, robust stomata detection algorithm which can also identify individual stomata boundaries regardless of the plant species, sample collection method, imaging technique and magnification level. Results: The proposed solution consists of three stages. First, the input image is pre-processed to remove any colour space biases occurring from different sample collection and imaging techniques. Then, a Mask R-CNN is applied to estimate individual stomata boundaries. The feature pyramid network embedded in the Mask R-CNN is utilised to identify stomata at different scales. Finally, a statistical filter is implemented at the Mask R-CNN output to reduce the number of false positive generated by the network. The algorithm was tested using 16 datasets from 12 sources, containing over 60,000 stomata. For the first time in this domain, the proposed solution was tested against 7 microscope datasets never seen by the algorithm to show the generalisability of the solution. Results indicated that the proposed approach can detect stomata with a precision, recall, and F-score of 95.10%, 83.34%, and 88.61%, respectively. A separate test conducted by comparing estimated stomata boundary values with manually measured data showed that the proposed method has an IoU score of 0.70; a 7% improvement over the bounding-box approach. Conclusions: The proposed method shows robust performance across multiple microscope image datasets of different quality and scale. This generalised stomata detection algorithm allows plant scientists to conduct stomata analysis whilst eliminating the need to re-label and re-train for each new dataset. The open-source code shared with this project can be directly deployed in Google Colab or any other Tensorflow environment.