Current Volume 4
India is an agricultural country and about seventy percent of our population depends on agriculture. One-third of our national income comes from agricultural. So the disease detection of plants plays an important role in the agricultural field. Majority of the plant diseases are caused by the attack of bacteria, fungi, virus etc. If proper care is not taken in this area, it may lead to serious effects on plants and adversely affects the productivity and quality. This paper propose an image pattern classification to identify the disease in leaf with combination of texture and color feature extractions. Firstly, normal and diseased images are collected and pre-processed. Then, features of shape, color and texture are extracted from these images. After that, these images are classified by Support Vector Machine(SVM) classifier. Image processing toolbox of Matlab is used for measuring affected area of disease and to determine the difference in the color of the disease affected area. The algorithm can be used to classify the leaves and the classified outcomes are separated using Arduino based Conveyor Belt system. This reduces the important task of monitoring of farms crops at very early stage itself to detect the symptom of diseases appear on plant leaves.
Support Vector Machine, Image Processing, Matlab, Arduino, Conveyor Belt.
Sudharshan Banakar , Sravani I , Thejashwini G M , Uthpala Mudenur "Green Leaf Disease Detection Using Image Processing and IOT" Iconic Research And Engineering Journals Volume 4 Issue 2 2020 Page 16-20
Sudharshan Banakar , Sravani I , Thejashwini G M , Uthpala Mudenur "Green Leaf Disease Detection Using Image Processing and IOT" Iconic Research And Engineering Journals, 4(2)