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Wheat grain classification by using dense SIFT features with SVM classifier
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DOI：10.1016/j.compag.2016.01.033.png)
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Abstract
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The demand for identification of cereal products with computer vision based applications has grown significantly over the last decade due to economic developments and reducing the labor force. With this regard, we have proposed an automated system that is capable to classify the wheat grains with the high accuracy rate. For this purpose, the performance of Dense Scale Invariant Features (DSIFT) is evaluated by concentrating on Support Vector Machine (SVM) classifier. First of all, the concept of k-means clustering is operated on DSIFT features and then images are represented with histograms of features by constituting the Bag of Words (BoW) of the visual words. By conducting an experimental study on a special data set, we can make a commitment that the proposed method provides the satisfactory results by achieving an overall 88.33% accuracy rate. (C) 2016 Elsevier B.V. All rights reserved.
Keywords:
Wheat identification
Grain classification
Dense SIFT features
k-means clustering
Support Vector Machines
Bag of Word Model
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