An Approach for Mango Disease Recognition using K-Means Clustering and SVM Classifier

Abstract

Bangladesh extensively depends on agriculture in terms of economy as well as food security for its huge population. For this reason, it is very important to efficiently grow a plant and enhance its yield. We often face some problem which need to be solved. We build a Mango Disease Recognition system which can recognize the mango disease. It's Very useful to the farmers because using this system they can easily identify their mango disease which is very important to produce more fruits. Using our system user can easily identify the problem and they can take action for better production. There also some existing project of similar topic but theses project are not available to the all users. More over some system recognize disease very poorly and there have less accuracy and it's a huge problem to use the system. Comparing other system our system can be use more efficiently. Recognition of Mango diseases poses two challenging problems, i.e. detection and classification of disease. In here we used K means clustering for feature extraction and SVM for classification. The novelty of our work is that here we recognize the mango diseases which is not existing and our project accuracy is 94.13%. So we think user will be benefited from our project to produce more product which can effect in our economy.