Open AccessDetection of Cotton Leaf Disease Using Image Processing Techniques
Author(s) -
Sushreeta Tripathy
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2062/1/012009
Subject(s) - thresholding , plant disease , segmentation , image processing , artificial intelligence , region of interest , computer science , image segmentation , computer vision , agricultural engineering , digital image processing , set (abstract data type) , agriculture , work (physics) , digital image , image (mathematics) , geography , microbiology and biotechnology , engineering , biology , programming language , mechanical engineering , archaeology
In the area of research, diagnosis of disease symptoms in the plants duly applying image processing methods is a matter of big concern. The need of the hour is to prepare an efficient plant disease diagnosis system that can help the farmers in their cultivation and farming. This work is an attempt to prepare a framework of plant disease diagnosis system by using the cotton plant leaves. The digital pictures of cotton leaves are obtained to undergo a set of image processing techniques. Thresholding based segmentation techniques are used to remove the region of interest (ROI) i.e., infected part from the enhanced images. Consequently, diseases are detected from the region of interest by using an accurate set of visual texture features. At last treatment actions are taken to supervise the diseases found in the plants. This work will help the farmer’s society to take effective measures to protect their crops from diseases.