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Each wood species has their special characteristics which can be differentiated based on their anatomical structures through wood identification. One of the methods is by detecting macroscopic wood image using computer vision. This method is more rapid and accurate to identify wood species compared to the conventional method. In previous work, we have developed a computer vision technique for wood identification by combining Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM). As smartphone usage is increasing worldwide, capturing wood structures using this smart device is very easy to do and can replace the use of digital microscopes. This paper proposes a technique for extraction the wood species on smartphone using combination of HOG method and SVM classifier on android smartphone. The SVM was used to classify the extracted wood textures from the HOG features. Our experiments, used seven wood images i.e Mimusops_elengi, Melanorrhoea wallichii, Acer niveum, Cratoxylon formosum, Agathis endertii, Dyera costulata and Knema glauca. Each species has a total of 100 training images and 100 testing images. The highest accuracy is obtained by Melanorrhoea wallichii and Agathis endertii species with 84 % score. The Agathis endertii species has the highest sensitivity and the value reaches 86%. Moreover, the Melanorrhoea wallichii species has a highest score for specificity and precision.

A Smartphone-Based of Wood Identification Using Image Feature Extraction