Automatic detection of rust disease of Lentil by machine learning system using microscopic images

Kuldeep Singh, Satish Kumar, Pawan Kaur


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. 


rust disease, microscopic image, local binary pattern, brightness bi-histogram, equalization, lentil

Full Text:


Total views : 431 times

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

ISSN 2088-8708, e-ISSN 2722-2578