scholarly journals A Deep Learning-Based Method for Automatic Assessment of Stomatal Index in Wheat Microscopic Images of Leaf Epidermis

2021 ◽  
Vol 12 ◽  
Author(s):  
Chuancheng Zhu ◽  
Yusong Hu ◽  
Hude Mao ◽  
Shumin Li ◽  
Fangfang Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Stomatal Density ◽  
Automatic Measurement ◽  
Epidermal Cells ◽  
Leaf Epidermis ◽  
Stomatal Index ◽  
Automatic Assessment ◽  
Microscopic Images ◽  
Index Measurement ◽  
Diagnostic Characteristics

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.

Micromorphologie des épidermes foliaires chez quelques espèces de Medicago

1983 ◽  
Vol 61 (12) ◽  
pp. 3461-3470 ◽  
Author(s):  
Catherine Damerval
Keyword(s):  
Guard Cell ◽  
Stomatal Density ◽  
Epidermal Cells ◽  
Cell Length ◽  
Leaf Epidermis ◽  
Trichome Density ◽  
Stomatal Index ◽  
Annual Species ◽  
Abaxial Epidermis ◽  
Stomatal Complex

The micromorphology of the abaxial epidermis of the first and sixth leaf has been studied in seven annual species of Medicago L. The pattern of the epidermal cells and of the stomatal complex does not allow differentiation of the taxa. Three main types of trichomes are recognized on the two foliar levels; their localization on the first leaf epidermis allows identification of five taxa out of seven. Four quantitative variables are also examined: stomatal density, trichome density, guard cell length, and stomatal index. The variable having the best discriminant value is the guard cell length on the first leaf. It is possible to identify each of the seven species by a combination of two features: the localization of the types of trichomes on the first leaf and the stomatal density on the sixth leaf.

scholarly journals Variation in Leaf Anatomical Traits of Pecan Cultivars

HortScience ◽  
2006 ◽  
Vol 41 (4) ◽  
pp. 999E-1000
Author(s):  
Madhulika Sagaram ◽  
Leonardo Lombardini ◽  
Larry Grauke
Keyword(s):  
Geographic Variation ◽  
Cell Density ◽  
Epidermal Cell ◽  
Stomatal Density ◽  
Epidermal Cells ◽  
Stomatal Index ◽  
Natural Range

An assessment of anatomical traits of pecan cultivars (`Pawnee', `Mohawk', and `Starking hardy giant') collected from three locations (Tifton, Ga.; Chetopa, Kans.; and Stillwater, Okla.) was conducted at Texas A&M University. The objective of the study was to provide an understanding of patterns of geographic variation within the natural range for anatomical (stomatal density, stomatal index, and epidermal cell density) traits. Microscopy using acetate casts was used as the means to investigate the patterns of variation in the epidermal characteristics of pecan leaf. `Starking hardy giant' had the greatest number of stomates/cm2 (46,229, 47,807, and 45,990 at Tifton, Chetopa, and Stillwater, respectively) while `Mohawk' had the least (37,397, 36,217, and 35,305). `Pawnee' had the greatest number of epidermal cells/cm2 (251,806, 250,098 and 254,883 at Tifton, Chetopa, and Stillwater, respectively) while `Starking hardy giant' had the least (141,699, 138,405, and 142,155). Differences in stomatal index were observed between the three cultivars at Tifton and Stillwater. No differences in stomatal index were observed between `Pawnee' and `Mohawk' at Chetopa. The study showed that stomatal density as well as epidermal cell density of all the tested cultivars were significantly different (P < 0.05) at a particular location but no differences were observed in a given cultivar grown at different locations.

scholarly journals Mutagenic effects of ultraviolet (UV-C) irradiation on the anatomy of three species of Capsicum

2019 ◽  
Vol 54 (2) ◽  
pp. 111-116
Author(s):  
KA Abdul Kareem ◽  
TJ Olobatoke ◽  
AA Abdul Rahaman ◽  
OT Mustapha
Keyword(s):  
Ultraviolet Irradiation ◽  
Cell Walls ◽  
Stomatal Density ◽  
Capsicum Frutescens ◽  
Leaf Epidermis ◽  
Stomatal Index ◽  
Stomatal Size ◽  
Leaf Surfaces ◽  
Mutagenic Effects ◽  
Uv C

UV radiant seedlings of Capsicum annuum, C. Chinenese and Capsicum frutescens were studied anatomically to observe the UV effects on the leaf epidermis, stem and root ultrastructures. While there is a higher percentage of stomatal index in the UV-exposed plants compared to the controlled, unexposed plants, there is no correlation in the stomatal density and stomatal size between the exposed and unexposed plants to the ultraviolet irradiation. There was also no correlation between the stomatal size and the stomatal density in both treatments (exposed and unexposed) in all the plants. Significant differences were observed in the stomatal index on both leaf surfaces between the exposed and controlled plants of C. frutescens and C. annuum. Cell walls of the stem and root wereobserved to be thicker in the UV-exposed plants. Bangladesh J. Sci. Ind. Res.54(2), 111-116, 2019

scholarly journals What is the influence of ordinary epidermal cells and stomata on the leaf plasticity of coffee plants grown under full-sun and shady conditions?

2010 ◽  
Vol 70 (4) ◽  
pp. 1083-1088 ◽  
Author(s):  
MF. Pompelli ◽  
SCV. Martins ◽  
EF. Celin ◽  
MC. Ventrella ◽  
FM. DaMatta
Keyword(s):  
Epidermal Cell ◽  
Stomatal Density ◽  
Land Plant ◽  
Epidermal Cells ◽  
Stomatal Index ◽  
Unit Leaf ◽  
Leaf Plasticity ◽  
Coffee Plants ◽  
Sun Leaves ◽  
Lower Irradiance

Stomata are crucial in land plant productivity and survival. In general, with lower irradiance, stomatal and epidermal cell frequency per unit leaf area decreases, whereas guard-cell length or width increases. Nevertheless, the stomatal index is accepted as remaining constant. The aim of this paper to study the influence of ordinary epidermal cells and stomata on leaf plasticity and the influence of these characteristics on stomata density, index, and sizes, in the total number of stomata, as well as the detailed distribution of stomata on a leaf blade. As a result, a highly significant positive correlation (R²a = 0.767 p < 0.001) between stomatal index and stomatal density, and with ordinary epidermal cell density (R²a = 0.500 p < 0.05), and a highly negative correlation between stomatal index and ordinary epidermal cell area (R²a = -0.571 p < 0.001), were obtained. However in no instance was the correlation between stomatal index or stomatal density and stomatal dimensions taken into consideration. The study also indicated that in coffee, the stomatal index was 19.09% in shaded leaves and 20.08% in full-sun leaves. In this sense, variations in the stomatal index by irradiance, its causes and the consequences on plant physiology were discussed.

scholarly journals Deep learning approach to describe and classify fungi microscopic images

PLoS ONE ◽  
2020 ◽  
Vol 15 (6) ◽  
pp. e0234806 ◽  
Author(s):  
Bartosz Zieliński ◽  
Agnieszka Sroka-Oleksiak ◽  
Dawid Rymarczyk ◽  
Adam Piekarczyk ◽  
Monika Brzychczy-Włoch
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Microscopic Images

scholarly journals Analysis of the Nosema Cells Identification for Microscopic Images

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3068
Author(s):  
Soumaya Dghim ◽  
Carlos M. Travieso-González ◽  
Radim Burget
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
The Other ◽  
Support Vector ◽  
Learning Approaches ◽  
Microscopic Images ◽  
Trained Neural Network ◽  
Nosema Disease

The use of image processing tools, machine learning, and deep learning approaches has become very useful and robust in recent years. This paper introduces the detection of the Nosema disease, which is considered to be one of the most economically significant diseases today. This work shows a solution for recognizing and identifying Nosema cells between the other existing objects in the microscopic image. Two main strategies are examined. The first strategy uses image processing tools to extract the most valuable information and features from the dataset of microscopic images. Then, machine learning methods are applied, such as a neural network (ANN) and support vector machine (SVM) for detecting and classifying the Nosema disease cells. The second strategy explores deep learning and transfers learning. Several approaches were examined, including a convolutional neural network (CNN) classifier and several methods of transfer learning (AlexNet, VGG-16 and VGG-19), which were fine-tuned and applied to the object sub-images in order to identify the Nosema images from the other object images. The best accuracy was reached by the VGG-16 pre-trained neural network with 96.25%.

Using Microscopic Images to Predict Plant Diseases in a Deep Learning Environment

2020 ◽  
pp. 807-813
Author(s):  
Priyadarshini Patil ◽  
Prashant Narayankar ◽  
Deepa Mulimani ◽  
Mayur Patil
Keyword(s):  
Deep Learning ◽  
Learning Environment ◽  
Plant Diseases ◽  
Microscopic Images

scholarly journals Growth and leaf epidermal response of three Sesamum indicum varieties to industrial effluent irrigation

2017 ◽  
Vol 52 (1) ◽  
pp. 1-6 ◽  
Author(s):  
AA Abdul Rahaman ◽  
OM Olaniran ◽  
FA Oladele
Keyword(s):  
Seedling Growth ◽  
Stomatal Density ◽  
Industrial Effluent ◽  
Industrial Effluents ◽  
Sesamum Indicum ◽  
Proper Treatment ◽  
Trichome Density ◽  
Stomatal Index ◽  
Stomatal Size ◽  
Epidermal Features

The effect of industrial effluents was studied with respect to growth and leaf anatomy of three Sesamum indicum varieties (NGB 00931, NGB 00937 and NGB 00939). Industrial effluents (25%, 50%, 75% and 100%) from two industries are used to irrigate the plants. Although, the control plants possessed larger leaves and longer stems than the effluent-treated plants, at lower concentration, the plant growth is relatively higher. Gradual decrease in the germination of seeds and seedling growth with increase in effluent concentration was observed. The best germination and seedling growth was observed at the 25% concentration. Leaf epidermal features (stomatal density, stomatal index, stomatal size, trichome density, tricome index, trichome size and number of epidermal cells) are more influenced in the effluent-treated plants than in the control plants from the Peace Standard Pharmaceutical Industry than in the effluent from the Global Soap & Detergent Industry. Thus the industrial effluents can be safely used for irrigation purposes with proper treatment and dilution at 25%.Bangladesh J. Sci. Ind. Res. 52(1), 1-6, 2017

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