Journal article Open Access

Automatic Tomato Plant Leaf Disease Classification using Multi-Kernel Support Vector Machine

Jayanthi M.G; Shashikumar D. R.


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    <subfield code="a">Tomato, Leaf disease, plant, median filter, texture, colour, shape, multi-kernel support vector machine.</subfield>
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    <subfield code="u">Computer Science and Engineering, Cambridge  Institute of technology,Visvesvaraya Technological University, Bangalore,  India</subfield>
    <subfield code="a">Shashikumar D. R.</subfield>
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    <subfield code="p">International Journal of Engineering and Advanced Technology (IJEAT)</subfield>
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    <subfield code="u">Computer Science and Engineering, Cambridge  Institute of technology,Visvesvaraya Technological University, Bangalore,  India</subfield>
    <subfield code="a">Jayanthi M.G</subfield>
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    <subfield code="a">Automatic Tomato Plant Leaf Disease  Classification using Multi-Kernel Support  Vector Machine</subfield>
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    <subfield code="a">&lt;p&gt;&lt;strong&gt;In agriculture the major problem is leaf disease identifying these disease in early stage increases the yield. To reduce the loss identifying the various disease is very important. In this work , an efficient technique for identifying unhealthy tomato leaves using a machine learning algorithm is proposed. Support Vector Machines (SVM) is the methodology of machine learning , and have been successfully applied to a number of applications to identify region of interest, classify the region. The proposed algorithm has three main staggers, namely preprocessing, feature extraction and classification. In preprocessing, the images are converted to RGB and the average filter is used to eliminate the noise in the input image. After the pre-processing stage, features such as texture, color and shape are extracted from each image. Then, the extracted features are presented to the classifier to classify an input tomato leaf as a healthy or unhealthy image. For classification, in this paper, a multi-kernel support vector machine (MKSVM) is used. The performance of the proposed method is analysed on the basis of different metrics, such as accuracy, sensitivity and specificity. The images used in the test are collected from the plant village. The proposed method implemented in MATLAB. &lt;/strong&gt;&lt;/p&gt;</subfield>
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