scholarly journals A Comparative Study of Chlorophyll Content Estimation in Barley (Hordeum vulgare L.) Genotypes Based on RGB (Red, Green, Blue) Image Analysis

2021 ◽  
Author(s):  
Ali Guendouz ◽  
Hocine Bendada ◽  
Ramadan Benniou
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Chlorophyll Content ◽  
Mean Squared Error ◽  
Low Cost ◽  
Vegetation Indices ◽  
Photochemical Reactions ◽  
Hordeum Vulgare L ◽  
Processing Techniques ◽  
Barley Genotypes

Background: Chlorophyll is the most important pigment in plant which absorbs light and subsequently transfers its energy to drive the photochemical reactions of photosynthesis. The numerical image processing techniques have been widely used in the analysis of leaf characteristics.Methods: The methods based on RGB (Red, Green and Blue) image analysis may emerge as a new and low-cost method for estimation the chlorophyll content. In this work, we use eight RGB vegetation indices as alternative for chlorophyll content estimation. Result: The student t-test showed that all the RGB indices tested are suitable to estimate the chlorophyll content in barley genotypes. In addition, the results which based on the correlation analysis in combination with the values of root mean squared error (RMSE) demonstrate that the very suitable RGB indices are these with high values of correlation coefficient and lowest values of RMSE. Data collected from barley genotypes leaves indicated that digital image processing technology can be a useful and rapid non-destructive method for assessment of chlorophyll content. Among the RGB indexes tested in this study the 100-(2R-B) and RGRI (R/G) are the most promising index to estimate the chlorophyll content in barley genotypes.

Image processing techniques used for dental x-ray image analysis

2008 ◽  
Author(s):  
Stefan Oprea ◽  
Costin Marinescu ◽  
Ioan Lita ◽  
Mariana Jurianu ◽  
Daniel Alexandru Visan ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
X Ray ◽  
Image Processing Techniques ◽  
Processing Techniques

scholarly journals IDENTIFICATION OF MEGALOBLASTIC ANEMIA CELLS THROUGH THE USE OF IMAGE PROCESSING TECHNIQUES

2018 ◽  
Vol 4 (3) ◽  
pp. 1-5 ◽  
Author(s):  
Asaad Babker ◽  
Vyacheslav Lyashenko
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Blood Smear ◽  
Color Image ◽  
Megaloblastic Anemia ◽  
Cell Structure ◽  
Color Space ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Blood Smear Image

Objective: Our aim is to show the possibility of using different image processing techniques for blood smear analysis. Also our aim is to determine the sequence of image processing techniques to identify megaloblastic anemia cells. Methods: We consider blood smear image. We use a variety of image processing techniques to identify megaloblastic anemia cells. Among these methods, we distinguish the modification of the color space and the use of wavelets. Results: We developed a sequence of image processing techniques for blood smear image analysis and megaloblastic anemia cells identification. As a characteristic feature for megaloblastic anemia cells identification, we consider neutrophil image structure. We also use the morphological methods of image analysis in order to reveal the nuclear lobes in neutrophil structure. Conclusion: We can identify the megaloblastic anemia cells. To do this, we use the following sequence of blood smear image processing: color image modification, change of the image contrast, use of wavelets and morphological analysis of the cell structure. 

Image Processing for Solar Cell Analysis, Diagnostics and Quality Assurance Inspection

2013 ◽  
pp. 338-375
Author(s):  
Michael G. Mauk
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Quality Assurance ◽  
Solar Cell ◽  
Process Development ◽  
Low Cost ◽  
Scale Up ◽  
Image Capturing ◽  
Infrared Cameras ◽  
Primary Focus

Image capturing, processing, and analysis have numerous uses in solar cell research, device and process development and characterization, process control, and quality assurance and inspection. Solar cell image processing is expanding due to the increasing performance (resolution, sensitivity, spectral range) and low-cost of commercial CCD and infrared cameras. Methods and applications are discussed, with primary focus on monocrystalline and polycrystalline silicon solar cells using visible and infrared (thermography) wavelengths. The most prominent applications relate to mapping of minority carrier lifetime, shunts, and defects in solar cell wafers, in various stages of the manufacturing process. Other applications include measurements of surface texture and reflectivity, surface cleanliness, integrity of metallization lines, uniformity of coatings, and crystallographic texture and grain size. Image processing offers the capability to assess large-areas (> 100 cm2) with a non-contact, fast (~ 1 second), and modest cost. The challenge is to quantify and interpret the image data in order to better inform device design, process engineering, and quality control. Many promising solar cell technologies fail in the transition from laboratory to factory due to issues related to scale-up in area and manufacturing throughput. Image analysis provides an effective method to assess areal uniformity, device-to-device reproducibility, and defect densities. More integration of image analysis from research devices to field testing of modules will continue as the photovoltaics industry matures.

scholarly journals Y A-T-Il Une Relation Entre Teneur En Chlorophylle Et Accumulation De La Proline Chez Les Céréales À Paille Sous Déficit Hydrique?

2018 ◽  
Vol 14 (15) ◽  
pp. 234
Author(s):  
Merabta Sarra ◽  
Zerafa Chafia ◽  
Benlaribi Mostefa
Keyword(s):  
Chlorophyll Content ◽  
Triticum Aestivum L ◽  
Hordeum Vulgare L ◽  
Test Conditions ◽  
Triticum Durum Desf ◽  
The Moment ◽  
Déficit Hydrique ◽  
The Relationship ◽  
Very High ◽  
Barley Genotypes

This paper focuses on the relationship between two genotypes of durum wheat (Triticum durum Desf.): Gamgoum Rekham (GGR) and Haurani; two genotypes of common wheat (Triticum aestivum L.): Florence aurore 8193 (FA) and Mexipak; and two genotypes of barley (Hordeum vulgare L.): Manel and Saïda 183. These genotypes were subjected to a water deficit during a period of twenty days at the heading stage. The proline content and the chlorophyll content are determined on the standard leaves both on the control and on the sample subjected to water stress after twenty days and after one and two weeks, following the return of watering. Despite the fact that these varieties are cultivated under the same conditions on the Algerian highlands, their reactions to the test conditions are very different. As a matter of fact, GGR and FA recorded very high levels of proline at the end of the stress. This, however, was at the moment when the two barley genotypes presented relatively low values for the two studied parameters. After the return of watering, the proline contents at the various genotypes returned gradually to those of the controls. The GGR genotype recovers more quickly than the others. As for the chlorophyll content, it evolves, unevenly, in the course of the test, showing no evolutionary indication in relation to the proline.

scholarly journals Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz)

2020 ◽  
Author(s):  
Michael Gomez Selvaraj ◽  
Manuel Valderrama ◽  
Diego Guzman ◽  
Milton Valencia ◽  
Henry Ruiz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Image Analysis ◽  
Vegetation Indices ◽  
Analytical Framework ◽  
Aerial Image ◽  
Root Yield ◽  
Breeding Programs ◽  
Cassava Root ◽  
Below Ground

Abstract Background: Rapid non-destructive measurements to predict cassava root yield over the full growing season through large numbers of germplasm and multiple environments is a huge challenge in Cassava breeding programs. As opposed to waiting until the harvest season, multispectral imagery using unmanned aerial vehicles (UAV) are capable of measuring the canopy metrics and vegetation indices (VIs) traits at different time points of the growth cycle. This resourceful time series aerial image processing with appropriate analytical framework is very important for the automatic extraction of phenotypic features from the image data. Many studies have demonstrated the usefulness of advanced remote sensing technologies coupled with machine learning (ML) approaches for accurate prediction of valuable crop traits. Until now, Cassava has received little to no attention in aerial image-based phenotyping and ML model testing. Results: To accelerate image processing, an automated image-analysis framework called CIAT Pheno-i was developed to extract plot level vegetation indices/canopy metrics. Multiple linear regression models were constructed at different key growth stages of cassava, using ground-truth data and vegetation indices obtained from a multispectral sensor. Henceforth, the spectral indices/features were combined to develop models and predict cassava root yield using different Machine learning techniques. Our results showed that (1) Developed CIAT pheno-i image analysis framework was found to be easier and more rapid than manual methods. (2) The correlation analysis of four phenological stages of cassava revealed that elongation (EL) and late bulking (LBK) were the most useful stages to estimate above-ground biomass (AGB), below-ground biomass (BGB) and canopy height (CH). (3) The multi-temporal analysis revealed that cumulative image feature information of EL+early bulky (EBK) stages showed a higher significant correlation (r = 0.77) for Green Normalized Difference Vegetation indices (GNDVI) with BGB than individual time points. Canopy height measured on the ground correlated well with UAV (CHuav)-based measurements (r = 0.92) at late bulking (LBK) stage. Among different image features, normalized difference red edge index (NDRE) data were found to be consistently highly correlated (r = 0.65 to 0.84) with AGB at LBK stage. (4) Among the four ML algorithms used in this study, k-Nearest Neighbours (kNN), Random Forest (RF) and Support Vector Machine (SVM) showed the best performance for root yield prediction with the highest accuracy of R2 = 0.67, 0.66 and 0.64, respectively. Conclusion: UAV platforms, time series image acquisition, automated image analytical framework (CIAT Pheno-i), and key vegetation indices (VIs) to estimate phenotyping traits and root yield described in this work have great potential for use as a selection tool in the modern cassava breeding programs around the world to accelerate germplasm and varietal selection. The image analysis software (CIAT Pheno-i) developed from this study can be widely applicable to any other crop to extract phenotypic information rapidly.

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