Local Binary Patterns Based Detection of Rust Disease of Lentils (Lens culinaris) Using k-NN Classification System
Journal Title: International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR) - Year 2017, Vol 7, Issue 4
Abstract
This research paper reported the role of k-Nearest Neighbor (k-NN) classifier for detection and classification of rust disease of Lens culinaris at microscopic level, which is a very initial stage of disease i.e. haustorium stage found in bean crops. Detection of the rust disease present on the surface of leaves was diagnosed at an early stage before going to spore stage, responsible for spreading of rust disease to the other plants. The average filter and Local Binary Patterns (LBP) were used for preprocessing and feature extraction, respectively, for detection of rust disease. For testing propose k-Nearest Neighbor (k-NN) classifier was used and the average classification accuracy found 91% of the test samples using k-NN. The work initiates automatic recognition of rust disease found in bean crops at a very early stage.
Authors and Affiliations
Kuldeep Singh, Satish Kumar, Pawan Kaur
Keywords
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- EP ID EP240271
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