A COMPARATIVE ANALYSIS OF LEAF DISEASE DETECTION USING IMAGE PROCESSING TECHNIQUE

2018 ◽  
Vol 5 (3) ◽  
pp. 40
Author(s):  
SAMIHA C. M. ◽  
S. P. PAVAN KUMAR ◽  
◽  
Keyword(s):  
Image Processing ◽  
Comparative Analysis ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Disease Detection ◽  
Leaf Disease

scholarly journals Identification of Leaf Disease Using Image Processing Technique

2021 ◽  
pp. 881-885
Author(s):  
Ankit Wagh ◽  
Ritwik Chumble ◽  
Santosh Kangane ◽  
Pranav Bakare
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Plant Disease ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Disease Detection ◽  
Revolutionary Change ◽  
Leaf Disease ◽  
Important Time

The increased availability of smartphones has made it easier for taking technology to every individual. Technology can play important role in the field of agriculture to improve outcomes. Plant disease is one of the important reasons for the decrement of yield. Bridging technology with agriculture can give revolutionary change. It is challenging to monitor the disease of plants manually. It consumes important time, resources, and need efforts. Hence faster image processing technique is used as a solution for it. Disease detection using image processing involves multiple steps like the acquisition of images, pre-processing, segmentation, feature extraction, and classification. This paper discusses a faster image processing technique using the KNN method. This algorithm gives an asymptotically faster method for image processing.

scholarly journals Harumanis mango leaf disease recognition system using image processing technique

2021 ◽  
Vol 23 (1) ◽  
pp. 378
Author(s):  
R. A. JM. Gining ◽  
S. S. M. Fauzi ◽  
N. M . Yusoff ◽  
T. R. Razak ◽  
M. H. Ismail ◽  
...  
Keyword(s):  
Image Processing ◽  
Recognition System ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Human Errors ◽  
Leaf Disease ◽  
Rgb Image

Current Harumanismango farming technique in Malaysia still mostlydepends on the farmers' own expertise to monitor the crops from the attack ofpests and insects. This approach is susceptible to human errors, and thosewho do not possess this skill may not be able to detect the disease at the righttime. As leaf diseases seriously affect the crop's growth and the quality of theyield, this study aims to develop a recognition system that detects thepresence of disease in the mango leaf using image processing technique.First, the image is acquired through a smartphone camera; once it has beenpre-processed, it is then segmented in which the RGB image is converted toan HSI image, then the features are extracted. Lastly, the classification ofdisease is done to determine thetype of leaf disease. The proposed systemeffectively detects and classify the disease with an accuracy of 68.89%. Thefindings of this project will contribute to farmers and society's benefit, andresearchers can use the approach to address similar issues in future works.

Handheld Device Based on Image Processing Technique for Detecting Multiple Diseases of Apple Leaves

2019 ◽  
Vol 16 (10) ◽  
pp. 4160-4163 ◽  
Author(s):  
Swati Singh ◽  
Sheifali Gupta ◽  
Rupesh Gupta
Keyword(s):  
Image Processing ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Disease Detection ◽  
Normal Leaf ◽  
Handheld Device ◽  
Apple Leaves ◽  
Occurrence Matrix ◽  
Conventional Detection

The present invention discloses a handheld device for multiple disease detection from apple leaf and method thereof. An algorithm is developed in combination with image processing and gray level co occurrence matrix for the classification of normal leaf and diseased apple leaf. The device performs image processing by segmentation of the image and then by using extracted features. The classification and detection of these diseases impart an early solution to the farmers leading less harm to the apple crops. Conventional Detection of the diseases through naked eye can sometimes be faulty. Therefore the device of present invention helps farmers to detect the accurate diseases and provide timely solutions for the same. The present invention increases the throughput and reduces subjectiveness of the previously used conventional methods by proving early and precise disease detection from apple leaves.

scholarly journals Classification of Leaf Disease from Image Processing Technique

2018 ◽  
Vol 10 (1) ◽  
pp. 191 ◽  
Author(s):  
Mahanijah Md Kamal ◽  
Ahmad Nor Ikhwan Masazhar ◽  
Farah Abdul Rahman
Keyword(s):  
Image Processing ◽  
Palm Oil ◽  
Oil Palm ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Support Vector ◽  
Leaf Disease ◽  
Clustering And Classification ◽  
Oil Sector ◽  
Palm Leaf

<p class="Abstract">Disease in palm oil sector is one of the major concerns because it affects the production and economy losses to Malaysia. Diseases appear as spots on the leaf and if not treated on time, cause the growth of the palm oil tree. This work presents the use of digital image processing technique for classification oil palm leaf disease sympthoms. Chimaera and Anthracnose is the most common symtoms infected the oil palm leaf in nursery stage. Here, support vector machine (SVM) acts as a classifier where there are four stages involved. The stages are image acquisition, image enhancement, clustering and classification. The classification shows that SVM achieves accuracy of 97% for Chimaera and 95% for Anthracnose.</p>

scholarly journals The Development of Diseases Identification System in Paddy Plant Using Image Processing Technique

2019 ◽  
Vol 2 ◽  
pp. 231-236
Author(s):  
Alex Wenda ◽  
Nanda Putri Miefthawati ◽  
Mas’ud Zein
Keyword(s):  
Image Processing ◽  
Processing Technique ◽  
Blast Disease ◽  
Brown Spot ◽  
Image Processing Technique ◽  
Identification System ◽  
Spot Disease ◽  
Leaf Disease ◽  
Brown Spot Disease ◽  
Processing Techniques

There are three types of paddy leaf disease that have similar symptoms, making it difficult for farmers to identify them, namely Blast Disease, Brown-Spot Disease, and Narrow Brown-Spot Disease. This paper aims to develop an application to identify paddy leaf disease automatically. Several important aspects of the development of software engineering such as usability, interactivity, and simplicity have been considered. Image processing techniques, namely Blobs analysis and color segmentation are used to get the characteristics of diseased leaf; these characteristics are then used to identify the type of diseases using a rule-based expert system. The results obtained indicate that the developed system recognition capability is considered satisfactory with an accuracy of 94.7%.

A New Image Processing Technique for STEM

1974 ◽  
Vol 32 ◽  
pp. 432-433
Author(s):  
Yasushi Kokubo ◽  
Hirotami Koike ◽  
Teruo Someya
Keyword(s):  
Electron Microscopy ◽  
Image Processing ◽  
Scanning Electron Microscopy ◽  
Elastic Scattering ◽  
Field Emission ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Energy Analyzer ◽  
Scanning Microscope ◽  
Scanning Electron

One of the advantages of scanning electron microscopy is the capability for processing the image contrast, i.e., the image processing technique. Crewe et al were the first to apply this technique to a field emission scanning microscope and show images of individual atoms. They obtained a contrast which depended exclusively on the atomic numbers of specimen elements (Zcontrast), by displaying the images treated with the intensity ratio of elastically scattered to inelastically scattered electrons. The elastic scattering electrons were extracted by a solid detector and inelastic scattering electrons by an energy analyzer. We noted, however, that there is a possibility of the same contrast being obtained only by using an annular-type solid detector consisting of multiple concentric detector elements.

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

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