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PERFORMANCE EVALUATION OF SEGMENTATION AND CLASSIFICATION OF TOBACCO SEEDLING DISEASES

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Author(s): MALLIKARJUNA P.B. and GURU D.S

Journal: International Journal of Machine Intelligence
ISSN 0975-2927

Volume: 3;
Issue: 4;
Start page: 204;
Date: 2011;
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Keywords: Image enhancement | CIELAB color model | Lesion area segmentation | Performance measures | Classification

ABSTRACT
In this paper, a new algorithm for segmentation of lesions on tobacco seedling leaves is proposed. Segmentation algorithm consists of mainly two steps. First step is to approximate lesion extraction using contrast stretching transformation and morphological operations such as erosion and dilation. Second step refines the outcome of first step by color segmentation using CIELAB color model. We have conducted a performance evaluation of segmentation algorithm by measuring the parameters such as Measure of overlapping (MOL), Measure of under-segmentation (MUS), Measure of over-segmentation (MOS), Dice similarity measure (DSM), Error-rate (ER), Precision (P) and Recall (R). Then first order statistical texture features are extracted from lesion area to detect and diagnose the disease type. These texture features are then used for classification purpose. A Probabilistic Neural Network (PNN) is employed to classify anthracnose and frog-eye spots present on tobacco seedling leaves. In order to corroborate the efficacy of the proposed model we have conducted an experimentation on a dataset of 1000 extracted areas of tobacco seedling leaves which are captured in an uncontrolled lighting conditions. Experimental results show that the proposed segmentation algorithm achieved best average DSM and MOL accuracy. The methodology presented herein effectively detected and classified the tobacco seedlings lesions upto an accuracy of 91.4412%. Further the recommended features are compared with Gray Level Co-occurrence Matrix (GLCM) based features to bring out their superiorities.
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