scholarly journals Maize Plant Desease Identification (Zea Mays L. Saccharata) Using Image Processing and K-Nearest Neighbor (K-Nn)

2018 ◽  
Vol 7 (3.20) ◽  
pp. 402
Author(s):  
Prihastuti Harsan ◽  
Arie Qurania ◽  
Karina Damayanti
Keyword(s):  
Image Processing ◽  
Zea Mays ◽  
Digital Image Processing ◽  
Digital Image ◽  
Plant Disease ◽  
Zea Mays L ◽  
Maize Plant ◽  
Computer Algorithms ◽  
K Nearest Neighbor ◽  
Plant Pests

Plant pests of maize are known to attack in all phases of corn plant growth (Zea mays L. saccharata), both vegetative and generative. Common pests found in maize are seed flies (Atherigona sp.), Stem borers (Ostrinia furnacalis), Boricoverpa armigera, leaf-eaters (Spodoptera litura). The process of identification of maize plant disease is done through laboratory analysis and direct observation. The time required to obtain the identification result is 4 (four) months. Plant pests will attack some parts of the plant, including leaves, stems and fruit. Early detection is usually done through leaves. Plant pests will attack the plant leaf area with certain characteristics. Digital image processing is the use of computer algorithms to perform image processing on digital images. Identification of maize plant disease can apply image processing techniques through the characteristics or symptoms of disease raised on the leaves. Characteristic of attacks by pests in maize plants can be detected through the colors and patterns that appear on the leaves. This research performs implementation of digital image processing method to identify disease in maize plant caused by pest. The disease is Hawar Leaf, Bulai (Downy Midew), Hama Grasshopper, Leaf Spot (Sourthern Leaf Blight). Through color and edge detection, the accuracy obtained is 91.7%. 

scholarly journals Using Satellite Image, Recognize and detect the vehicles in Digital Image Processing by applying Otsu Threshold Method

2019 ◽  
Vol 8 (6S) ◽  
pp. 1084-1086
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Video Processing ◽  
Satellite Images ◽  
Satellite Image ◽  
Data Gathering ◽  
Computer Algorithms ◽  
Threshold Method ◽  
Color Processing

digital image processing (DIP) is known as a process which uses several computer algorithms. Image processing is done on digital images by using these algorithms. Digital image processing is using in several applications, like image-sharpening, medical, pattern reorganization, color processing, remote sensing, video processing etc. The traffic data is affected by satellite images object oriented detection approach and satellite resolution. As compared with the conventional data gathering approach when data is gathering from satellite images then it can be process more quickly and efficiently. The research works is done for detecting and recognize the vehicle in satellite images. The threshold technique that using in this research is Otsu method. The main objective for this approach to find a more improved and effective approach to detect the vehicles in less time.

scholarly journals Digital image processing methods for the evaluation of optical anisotropy effects in tempered architectural glass using photoelastic measurements

2021 ◽  
Vol 6 (1) ◽  
pp. 3-19
Author(s):  
S. Dix ◽  
P. Müller ◽  
C. Schuler ◽  
S. Kolling ◽  
J. Schneider
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Optical Anisotropy ◽  
General Definition ◽  
Textural Features ◽  
Computer Algorithms ◽  
Image Size ◽  
Toughened Glass ◽  
Processing Methods

AbstractIn the present paper, optical anisotropy effects in architectural glass are evaluated using digital image processing. Hereby, thermally toughened glass panes were analyzed quantitatively using a circular polariscope. Glass subjected to externally applied stresses or residual stresses becomes birefringent. Polarized light on birefringent materials causes interference colors (iridescence), referred to as anisotropies, which affect the optical appearance of glass panes in building envelopes. Thermally toughened glass, such as toughened safety glass or heat strengthened glass, show these iridescences due to thermally induced residual stress differences. RGB-photoelastic full-field methods allow the quantitative measurement of anisotropies, since the occurring interference colors are related to the measured retardation values. By calibrating the circular polariscope, retardation images of thermally toughened glass panes are generated from non-directional isochromatic images using computer algorithms. The analysis of the retardation images and the evaluation of the anisotropy quality of the glass is of great interest in order to detect and sort out very low quality glass panes directly in the production process. Therefore, in this paper retardation images are acquired from different thermally toughened glass panes then different image processing methods are presented and applied. It is shown that a general definition of exclusion zones, e.g. near edges is required prior to the evaluation. In parallel, the limitations in the application of first-order statistical and threshold methods are presented. The intend of the investigation is the extension of the texture analysis based on the generation of Grey Level Co-occurrence Matrices, where the spatial arrangement of the retardation values is considered in the evaluation. For the first time, the results of textural features of different glass pane formats could be compared using reference areas and geometry factors. By reduction of the original image size, the computation time of textural analysis algorithms could be remarkably speeded up, while the textural features remained the same. Finally, the knowledge gained from these investigations is used to determine uniform texture features, which also includes the pattern of anisotropy effects in the evaluation of thermally toughened glass. Together with a global evaluation criterion this can now be implemented in commercial anisotropy measurement systems for quality control of tempered architectural glass.

Intelligent System for Accurate Prediction and Detection of Alzheimer’s Disease

2019 ◽  
pp. 51-56
Author(s):  
Anusha Nambirajam K ◽  
Siva Subramanian T ◽  
Priyadharshini R
Keyword(s):  
Alzheimer’S Disease ◽  
Image Processing ◽  
Alzheimer's Disease ◽  
Digital Image Processing ◽  
Digital Image ◽  
Intelligent System ◽  
Essential Feature ◽  
Memory Loss ◽  
Video Frame ◽  
K Nearest Neighbor

Image processing is a process of converting an image into digital form and achieve some maneuvers on it, in order to get an enhanced image or to mine some useful information from it. It is a type of signal dispensation in which the input is an image, like video frame or photograph and output, may be image along with its characteristics and features associated with that image. An image is defined as a two-dimensional function F(x,y), where x and y are spatial coordinates, and the amplitude of F at any pair of coordinates (x,y) is called the intensity of that image at that point. When x, y, and amplitude values of F are finite, we call it a digital image. Image processing mainly consists of three basic steps. They are as follows: Initially, the image will be imported by using an optical scanner or by high-digital photography. Then the captured image will be subjected to the analyzation and manipulation process. These process also includes compression of data, enhancement of the image and spotting the patterns that are not visible to human eyes like satellite photography. Finally, the output will be obtained as an alternative image or any other essential feature extraction of the pre-processed image. Image Processing consists of two major types. They are  Analog Image Processing and Digital Image Processing. Digital Image Processing is a process in which a digital system is developed for processing a digital image and extracting feature form of results. Digital Image Processing works on the basis of an algorithm. An Intelligent System for Accurate Detection and Prediction of Alzheimer’s Disease mainly uses the k-nearest neighbor algorithm. Alzheimer’s  Disease is a type of disease in which the brain cells tend to die away and cause memory loss. In our proposed model we predict the accuracy of the amount of memory loss occurred in an affected brain. This system is mainly developed for helping the doctors and psychologists to obtain a maximum level of accuracy of the patient’s affected brain.

An Overview of Digital Image Processing and Recognition (Invited Paper)

1982 ◽  
Vol 40 ◽  
pp. 700-702
Author(s):  
R. C. Gonzalez
Keyword(s):  
Image Processing ◽  
New York ◽  
Digital Image Processing ◽  
Digital Image ◽  
Practical Implementation ◽  
Submarine Cable ◽  
Vigorous Growth ◽  
Digitized Pictures ◽  
Processing Techniques ◽  
And Storage

Interest in digital image processing techniques dates back to the early 1920's, when digitized pictures of world news events were first transmitted by submarine cable between New York and London. Applications of digital image processing concepts, however, did not become widespread until the middle 1960's, when third-generation digital computers began to offer the speed and storage capabilities required for practical implementation of image processing algorithms. Since then, this area has experienced vigorous growth, having been a subject of interdisciplinary research in fields ranging from engineering and computer science to biology, chemistry, and medicine.

A method to measure pores and fissures in geologic materials under S.E.M. by digital image processing

1978 ◽  
Vol 36 (1) ◽  
pp. 212-213
Author(s):  
L. Montoto ◽  
M. Montoto ◽  
A. Bel-Lan
Keyword(s):  
Image Processing ◽  
Optical Microscopy ◽  
Digital Image Processing ◽  
Digital Image ◽  
Digital Processing ◽  
Free Surfaces ◽  
Geologic Materials ◽  
Automatic Information ◽  
Detector Output ◽  
Rock Specimens

INTRODUCTION.- The physical properties of rock masses are greatly influenced by their internal discontinuities, like pores and fissures. So, these need to be measured as a basis for interpretation. To avoid the basic difficulties of measurement under optical microscopy and analogic image systems, the authors use S.E.M. and multiband digital image processing. In S.E.M., analog signal processing has been used to further image enhancement (1), but automatic information extraction can be achieved by simple digital processing of S.E.M. images (2). The use of multiband image would overcome difficulties such as artifacts introduced by the relative positions of sample and detector or the typicals encountered in optical microscopy.DIGITAL IMAGE PROCESSING.- The studied rock specimens were in the form of flat deformation-free surfaces observed under a Phillips SEM model 500. The SEM detector output signal was recorded in picture form in b&w negatives and digitized using a Perkin Elmer 1010 MP flat microdensitometer.

Digital image processing methods applied to the study of annealing time on semiconductor-metal eutectic composites

1988 ◽  
Vol 46 ◽  
pp. 848-849
Author(s):  
J. Hefter
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Electronic Device ◽  
Processing System ◽  
Average Diameter ◽  
Eutectic Solidification ◽  
Metal Silicide ◽  
The Matrix ◽  
Microscopic Studies

Semiconductor-metal composites, formed by the eutectic solidification of silicon and a metal silicide have been under investigation for some time for a number of electronic device applications. This composite system is comprised of a silicon matrix containing extended metal-silicide rod-shaped structures aligned in parallel throughout the material. The average diameter of such a rod in a typical system is about 1 μm. Thus, characterization of the rod morphology by electron microscope methods is necessitated.The types of morphometric information that may be obtained from such microscopic studies coupled with image processing are (i) the area fraction of rods in the matrix, (ii) the average rod diameter, (iii) an average circularity (roundness), and (iv) the number density (Nd;rods/cm2). To acquire electron images of these materials, a digital image processing system (Tracor Northern 5500/5600) attached to a JEOL JXA-840 analytical SEM has been used.

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