Open AccessLeaf Disease Detection of Agricultural plant Using Image Processing
Author(s) -
Aneel Narayanapur,
Pavankumar Naik,
Priya B Kori,
Naseem Kalaburgi,
I M Rubiya,
M. Madhu
Publication year - 2020
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit2063113
Subject(s) - rgb color model , transformation (genetics) , artificial intelligence , pixel , computer science , computer vision , biology , plant disease , image processing , pattern recognition (psychology) , image (mathematics) , botany , microbiology and biotechnology , genetics , gene
The detection of plant leaf is an very important factor to prevent serious outbreak. Automatic detection of plant disease is essential research topic. Most plant diseases are caused by fungi, bacteria, and viruses. Fungi are identified primarily from their morphology, with emphasis placed on their reproductive structures. Bacteria are considered more primitive than fungi and generally have simpler life cycles. With few exceptions, bacteria exist as single cells and increase in numbers by dividing into two cells during a process called binary fission Viruses are extremely tiny particles consisting of protein and genetic material with no associated protein. The term disease is usually used only for the destruction of live plants. The developed processing scheme consists of four main steps, first a color transformation structure for the input RGB image is created, this RGB is converted to HSI because RGB is for color generation and his for color descriptor. Then green pixels are masked and removed using specific threshold value, then the image is segmented and the useful segments are extracted, finally the texture statistics is computed. from SGDM matrices. Finally the presence of diseases on the plant leaf is evaluated.