Open AccessAutomatic detection of rust disease of Lentil by machine learning system using microscopic images
Author(s) -
Karamjit Singh,
Satish Kumar,
Pawan Kaur
Publication year - 2019
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v9i1.pp660-666
Subject(s) - rust (programming language) , artificial intelligence , feature (linguistics) , pattern recognition (psychology) , computer science , image processing , histogram equalization , local binary patterns , set (abstract data type) , histogram , matlab , plant disease , haustorium , image (mathematics) , computer vision , biology , microbiology and biotechnology , host (biology) , ecology , philosophy , linguistics , programming language , operating system
Accurate and early detection of plant diseases will facilitate mitigate the worldwide losses experienced by the agriculture area. MATLAB image processing provides quick and non-destructive means of rust disease detection. In this paper, microscopic image data of rust disease of Lentil was combined with image processing with depth information and developed a machine learning system to detect rust disease at early stage infected with fungus Uromyces fabae (Pers) de Bary. A novel feature set was extracted from the image data using local binary pattern (LBP) and HBBP (Brightness Bi-Histogram Equalization) for image enhancement. It was observed that by combining these, the accuracy of detection of the diseased plants at microscopic level was significantly improved. In addition, we showed that our novel feature set was capable of identifying rust disease at haustorium stage without spreading of disease.