Classification and Grading of Wheat Granules using SVM and Naive Bayes Classifier

Abstract

India is the second leading producer of wheat in the world. Specifying the quality of wheat manually is very time consuming and requires an expert judgment. With the help of image processing techniques, a system can be made to avoid the human inspection. Classification of wheat grains is carried out according to their grades to determine the quality. Images are acquired for wheat grains using digital camera. Conversions to gray scale, Smoothing, Thresholding, Canny edge detection are the checks that are performed on the acquired image using image processing technique. Classification and Grading of wheat grain is carried out by extracting morphological, color and texture features. These features are given to SVM and Naive Bayes Classifier for classification. To evaluate the classification accuracy, from the total of 1300 data sets 50% were used for training and the remaining 50% was used for testing. The classification system was supervised corresponding to the predefined classes of grades. Results showed that overall accuracy of SVM and Naive Bayes classifier is 94.45%, 92.60% respectively. So, the classification performance of SVM is better than Naive Bayes Classifier.
DOI: 10.17762/ijritcc2321-8169.150850