IDENTIFICATION AND RECOGNITION OF LEAF DISEASE USING ENHANCED SEGMENTATION TECHNIQUES
Abstract
vioft2nntf2t|tblJournal|Abstract_paper|0xf4ffa5b42e0000001455100001000400
Segmenting refers to the technique of breaking up an image into its component parts one by one. When it comes to the process of segmenting photos, there is a plethora of choice available at current point in time. These options range from the easy thresholding approach to the complicated color image segmentation techniques. The bulk of the time, the parts that go into making up these sub-assemblies are items that individuals are able to easily identify and categorize as being distinct from one another. As a result of the limitation of computer lack of intelligence to differentiate between distinct items, a wide variety of techniques have been devised and utilized in the process of segmenting photographs. In order to complete its tasks, the image segmentation algorithm requires a wide range of image characteristics to be provided as input. This could be referring to the colors that are contained within an image, the borders that are included within the image, or a particular region that is contained within the image. In order to break down color images into their component elements, we make use of an algorithm that is inspired by natural selection. The research uses enhanced segmentation techniques to identify and recognize the leaf disease in plants. The study conducts extensive simulation to test the efficacy of the model. The results show that the proposed method achieves higher segmentation accuracy than other methods.

Authors
Brijendra Gupta1, V. Elanangai2, G.N. Naveen Kumar3, P.T. Kalaivaani4
Siddhant College of Engineering, India1, St. Peter’s Institute of Higher Education and Research, India2, CMR Institute of Technology, India3, Vivekanandha College of Engineering for Women, India 4

Keywords
Segmentation, Images, Leaf Disease
Yearly Full Views
JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecember
210000000000
Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 13 , Issue: 2 , Pages: 2825 - 2830 )
Date of Publication :
November 2022
Page Views :
112
Full Text Views :
4

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.