Open AccessIdentification of Plant Leaf Disease using Machine Learning Techniques
Author(s) -
Shabari Shedthi B*,
M siddappa,
Surendra Shetty
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c5621.098319
Subject(s) - artificial intelligence , support vector machine , identification (biology) , computer science , machine learning , pattern recognition (psychology) , segmentation , naive bayes classifier , artificial neural network , plant disease , feature extraction , feature (linguistics) , microbiology and biotechnology , biology , linguistics , philosophy , botany
Plant disease identification and classification is major area of research as majority of people in India depend on agriculture for their main source of income and for food. Identification of the diseases in any crops is challenging since manual identification techniques being used in this are based on the experts advises which may not be efficient. Based on leaf features decisions about variety of diseases are taken. In this paper an automated framework is introduced which can be used to detect and classify the diseases in the leaf accurately. Leaf images are acquired by using digital camera. Pre-processing techniques, segmentation and feature extraction are performed on the acquired images. The features are passed on to the classifiers to classify the diseases. This work has been proposed to classify and distinguish the leaf sample based on its features. The proposed work is carried out with Artificial Neural Network (ANN), Support Vector Machine (SVM) and Naive Bayes classifiers to analyze the result. For given dataset ANN performed better than the other two classifiers