Open AccessA STUDY OF TOMATO FRUIT DISEASE DETECTION USING RGB COLOR THRESHOLDING AND K-MEANS CLUSTERING
Author(s) -
S. Lingeswari,
P. Gomathi,
Siddharth Kailasam
Publication year - 2021
Publication title -
international journal of computer science and mobile computing
Language(s) - English
Resource type - Journals
ISSN - 2320-088X
DOI - 10.47760/ijcsmc.2021.v10i08.009
Subject(s) - thresholding , rgb color model , cluster analysis , artificial intelligence , computer science , image processing , image segmentation , focus (optics) , computer vision , segmentation , pattern recognition (psychology) , image (mathematics) , physics , optics
The agriculture field plays vital role in development of smart India. To increase economic level the production of fruits, crops and vegetables can use CAD technique using image processing tools. Identifying diseases in fruits is an image processing’s big challenging task. This can done by continuous visual photos or videos monitoring system. The automated image processing research helps to control the pesticides on fruits and vegetables. In this paper we focus to detect the diseases of tomato at earlier stage. The proposed system shows how different algorithms such as color thresholding segmentation techniques and K-means clustering are used. In proposed system shows the K-means Clustering is better than RGB color based colorthresholder method for detecting tomato diseases in beginning stage.