Measuring and analyzing color and texture information in anatomical leaf cross sections: an approach using computer vision to aid plant species identification

Botany ◽  
2011 ◽  
Vol 89 (7) ◽  
pp. 467-479 ◽  
Author(s):  
Jarbas Joaci de M. Sá Junior ◽  
André R. Backes ◽  
Davi Rodrigo Rossatto ◽  
Rosana M. Kolb ◽  
Odemir M. Bruno
Keyword(s):  
Cross Sections ◽  
Computational Approach ◽  
Information Structures ◽  
Plant Identification ◽  
Linear Discriminant ◽  
Central Brazil ◽  
Phylogenetic Studies ◽  
Leaf Tissues ◽  
Plant Species Identification ◽  
Leave One Out

Currently, studies on leaf anatomy have provided an important source of characters helping taxonomic, systematic, and phylogenetic studies. These studies strongly rely on measurements of characters (such as tissue thickness) and qualitative information (structures description, presence–absence of structures). In this work, we provide a new computational approach that semiautomates the collection of some quantitative data (cuticle, adaxial epidermis, and total leaf thickness) and accesses a new source of information in leaf cross-section images: the texture and the color of leaf tissues. Our aim was to evaluate this information for plant identification purposes. We successfully tested our system identifying eight species from different phylogenetic positions in the angiosperm phylogeny from the neotropical savanna of central Brazil. The proposed system checks the potential of identifying the species for each extracted measure using the Jeffrey–Matusita distance and composes a feature vector with the most important metrics. A linear discriminant analysis with leave-one-out to classify the samples was used. The experiments achieved a 100% success rate in terms of identifying the studied species accessing the above-described parameters, demonstrating that our computational approach can be a helpful tool for anatomical studies, especially ones devoted to plant identification and systematic studies.

Mobile Vision for Plant Biometric System

2020 ◽  
pp. 773-791
Author(s):  
Shitala Prasad
Keyword(s):  
Ubiquitous Computing ◽  
Species Identification ◽  
Plant Species ◽  
Expert Knowledge ◽  
Biometric System ◽  
Plant Identification ◽  
Significant Growth ◽  
Wide Range ◽  
Plant Species Identification ◽  
Research Analysis

In human's life plant plays an important part to balance the nature and supply food-&-medicine. The traditional manual plant species identification method is tedious and time-consuming process and requires expert knowledge. The rapid developments of mobile and ubiquitous computing make automated plant biometric system really feasible and accessible for anyone-anywhere-anytime. More and more research are ongoing to make it a more realistic tool for common man to access the agro-information by just a click. Based on this, the chapter highlights the significant growth of plant identification and leaf disease recognition over past few years. A wide range of research analysis is shown in this chapter in this context. Finally, the chapter showed the future scope and applications of AaaS and similar systems in agro-field.

scholarly journals Discriminant Analysis of Traditional Chinese Medicinal Properties Based on Holistic Chemical Profiling by 1H-NMR Spectrometry

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Jie Zhang ◽  
Wenna Guo ◽  
Qiao Li ◽  
Faxin Sun ◽  
Xiaomeng Xu ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Chinese Medicine ◽  
Principal Component ◽  
Linear Discriminant ◽  
Chemical Profiling ◽  
H Nmr ◽  
Nmr Spectrometry ◽  
Cinnamomi Cortex ◽  
Medicinal Properties ◽  
Leave One Out

Medicinal property, which is closely related to drug chemical profiling, is the essence of traditional Chinese medicine (TCM) theory and has always been the focus of modern Chinese medicine. Based on dozens of classic and commonly used TCM herbs with recognized medicinal properties, the present study just aimed to investigate the feasibility and reliability of medicinal property discriminant by using 1H-NMR spectrometry, which provided a mass of spectral data showing holistic chemical profile for multivariate analysis and data mining, including principal component analysis (PCA), Fisher linear discriminant analysis (FLDA), and canonical discriminant analysis (CDA). By using FLDA for two-class recognition, a large majority of test herbs (59/61) were properly discriminated as cold or hot group, and the only two exceptions were Chuanbeimu (Fritillariae Cirrhosae Bulbus) and Rougui (Cinnamomi Cortex), suggesting that medicinal properties interrelate with flavor and body tropism, and all these factors together bring up medicinal property and efficacy. While by performing CDA, 98.4% of the original grouped herbs and 77.0% of the leave-one-out cross-validated grouped cases were correctly classified. The findings demonstrated that discriminant analysis based on holistic chemical profiling data by 1H-NMR spectrometry may provide a powerful alternative to have a deeper understanding of TCM medicinal property.

scholarly journals ADAPTIVE VARIABLE EXTRACTIONS WITH LDA FOR CLASSIFICATION OF MIXED VARIABLES, AND APPLICATIONS TO MEDICAL DATA

2021 ◽  
Vol 20 (Number 3) ◽  
pp. 305-327
Author(s):  
Hashibah Hamid ◽  
Nor Idayu Mahat ◽  
Safwati Ibrahim
Keyword(s):  
Multiple Correspondence Analysis ◽  
Principal Component ◽  
Medical Data ◽  
Significant Shift ◽  
Continuous Variables ◽  
Linear Discriminant ◽  
Mixed Variables ◽  
Constant Change ◽  
Leave One Out ◽  
Direct Use

The strategy surrounding the extraction of a number of mixed variables is examined in this paper in building a model for Linear Discriminant Analysis (LDA). Two methods for extracting crucial variables from a dataset with categorical and continuous variables were employed, namely, multiple correspondence analysis (MCA) and principal component analysis (PCA). However, in this case, direct use of either MCA or PCA on mixed variables is impossible due to restrictions on the structure of data that each method could handle. Therefore, this paper executes some adjustments including a strategy for managing mixed variables so that those mixed variables are equivalent in values. With this, both MCA and PCA can be performed on mixed variables simultaneously. The variables following this strategy of extraction were then utilised in the construction of the LDA model before applying them to classify objects going forward. The suggested models, using three real sets of medical data were then tested, where the results indicated that using a combination of the two methods of MCA and PCA for extraction and LDA could reduce the model’s size, having a positive effect on classifying and better performance of the model since it leads towards minimising the leave-one-out error rate. Accordingly, the models proposed in this paper, including the strategy that was adapted was successful in presenting good results over the full LDA model. Regarding the indicators that were used to extract and to retain the variables in the model, cumulative variance explained (CVE), eigenvalue, and a non-significant shift in the CVE (constant change), could be considered a useful reference or guideline for practitioners experiencing similar issues in future.

scholarly journals Rapid Identification of COVID-19 Severity in CT Scans through Classification of Deep Features

2020 ◽  
Author(s):  
Zekuan Yu ◽  
Xiaohu Li ◽  
Haitao Sun ◽  
Jian Wang ◽  
Tongtong Zhao ◽  
...  
Keyword(s):  
Cross Validation ◽  
Rapid Identification ◽  
Ct Scans ◽  
Accurate Identification ◽  
Linear Discriminant ◽  
Holdout Validation ◽  
Novel Coronavirus ◽  
Leave One Out ◽  
Fold Cross Validation

Abstract Background: To implement the real-time diagnosis of the severity of patients infected with novel coronavirus 2019 (COVID-19) and guide the follow-up therapeutic treatment, We collected chest CT scans of 202 patients diagnosed with the COVID-19 from three hospitals in Anhui Province, China.Methods: A total of 729 2D axial plan slices with 246 severe cases and 483 non-severe cases were employed in this study. Four pre-trained deep models (Inception-V3, ResNet-50, ResNet-101, DenseNet-201) with multiple classifiers (linear discriminant, linear SVM, cubic SVM, KNN and Adaboost decision tree) were applied to identify the severe and non-severe COVID-19 cases. Three validation strategies (holdout validation, 10-fold cross-validation and leave-one-out) are employed to validate the feasibility of proposed pipelines. Results and conclusion: The experimental results demonstrate that classification of the features from pre-trained deep models show the promising application in COVID-19 screening whereas the DenseNet-201 with cubic SVM model achieved the best performance. Specifically, it achieved the highest severity classification accuracy of 95.20% and 95.34% for 10-fold cross-validation and leave-one-out, respectively. The established pipeline was able to achieve a rapid and accurate identification of the severity of COVID-19. This may assist the physicians to make more efficient and reliable decisions.

scholarly journals Anatomical Changes on the Stem and Leaves of Solanum lycopersicum Caused by Different Concentrations of Picloram + 2,4-D, in Two Different Types of Soil

2018 ◽  
Vol 36 (0) ◽  
Author(s):  
A.C. BATISTÃO ◽  
O.M. YAMASHITA ◽  
I.V. SILVA ◽  
C.F. ARAÚJO ◽  
A. LAVEZO
Keyword(s):  
Cross Sections ◽  
Clay Loam ◽  
Intercellular Spaces ◽  
Mato Grosso ◽  
Anatomical Structures ◽  
Anatomical Changes ◽  
Prolonged Effect ◽  
Different Types ◽  
Leaf Tissues ◽  
Statistical Program

ABSTRACT: Contamination by herbicides with a prolonged effect on the soil can cause anatomical changes in sensitive plants. Thus, this study aimed at verifying the anatomical changes of tomato stem and leaves caused by different concentrations of picloram in two classes of soil from the Amazon region. The study was developed at UNEMAT, Alta Floresta - Mato Grosso state, in a CRD, in a 2 x 5 factorial arrangement, with four replications. A clayey Rhodic Hapludox (LVAw) and a sandy clay loam Typic Ustipsamments (RQo) were contaminated with 0, 1, 2, 3, and 4 L ha-1 of Tordon®, leaving the soil exposed to weathering. One-hundred and twenty days after the application of the herbicide, 10 tomato seeds were sown in samples of both soils. Thirty days after sowing, cross sections of stem and leaf were fixed in FAA50, immersed in methacrylate, cut into a rotary microtome and stained with toluidine blue. The thickness of stem and leaf tissues was analyzed. Data were submitted to analysis of variance and regression analysis by the statistical program Sisvar. The increase in the concentration of picloram caused an increase in the thickness of the leaf blade and in the vascular bundle of the leaf in both soils, with greater effect in the LVAw, where there was tissue disorganization, with irregular and quite collapsed lacunar parenchyma cells and large intercellular spaces. There was also an increase in the diameter of the cortex and in the vascular cylinder of the stem up to the concentration of 2 L ha-1, but in the RQo, plants had more flattened cells with conspicuous intercellular spaces. The anatomical structures of the leaf were more affected by this herbicide.

scholarly journals Plant Identification Using New Architecture Convolutional Neural Networks Combine with Replacing the Red of Color Channel Image by Vein Morphology Leaf

2020 ◽  
Vol 07 (02) ◽  
pp. 197-208
Author(s):  
Hiep Xuan Huynh ◽  
Bao Quoc Truong ◽  
Kiet Tan Nguyen Thanh ◽  
Dinh Quoc Truong
Keyword(s):  
Neural Network ◽  
Species Identification ◽  
Plant Species ◽  
Plant Identification ◽  
Color Channel ◽  
Data Set ◽  
Plant Species Identification ◽  
Channel Image ◽  
Shape Data

The determination of plant species from field observation requires substantial botanical expertise, which puts it beyond the reach of most nature enthusiasts. Traditional plant species identification is almost impossible for the general public and challenging even for professionals who deal with botanical problems daily such as conservationists, farmers, foresters, and landscape architects. Even for botanists themselves, species identification is often a difficult task. This paper proposes a model deep learning with a new architecture Convolutional Neural Network (CNN) for leaves classifier based on leaf pre-processing extract vein shape data replaced for the red channel of colors. This replacement improves the accuracy of the model significantly. This model experimented on collector leaves data set Flavia leaf data set and the Swedish leaf data set. The classification results indicate that the proposed CNN model is effective for leaf recognition with the best accuracy greater than 98.22%.

scholarly journals Electronic Eye Based on RGB Analysis for the Identification of Tequilas

Biosensors ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 68
Author(s):  
Anais Gómez ◽  
Diana Bueno ◽  
Juan Manuel Gutiérrez
Keyword(s):  
Low Cost ◽  
Principal Component ◽  
Image Sensor ◽  
Raspberry Pi ◽  
Classification Rate ◽  
Linear Discriminant ◽  
Qualitative Classification ◽  
Analytical System ◽  
Leave One Out ◽  
Electronic Eye

The present work reports the development of a biologically inspired analytical system known as Electronic Eye (EE), capable of qualitatively discriminating different tequila categories. The reported system is a low-cost and portable instrumentation based on a Raspberry Pi single-board computer and an 8 Megapixel CMOS image sensor, which allow the collection of images of Silver, Aged, and Extra-aged tequila samples. Image processing is performed mimicking the trichromatic theory of color vision using an analysis of Red, Green, and Blue components (RGB) for each image’s pixel. Consequently, RGB absorbances of images were evaluated and preprocessed, employing Principal Component Analysis (PCA) to visualize data clustering. The resulting PCA scores were modeled with a Linear Discriminant Analysis (LDA) that accomplished the qualitative classification of tequilas. A Leave-One-Out Cross-Validation (LOOCV) procedure was performed to evaluate classifiers’ performance. The proposed system allowed the identification of real tequila samples achieving an overall classification rate of 90.02%, average sensitivity, and specificity of 0.90 and 0.96, respectively, while Cohen’s kappa coefficient was 0.87. In this case, the EE has demonstrated a favorable capability to correctly discriminated and classified the different tequila samples according to their categories.

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