Tomatoes classification using K-NN based on GLCM and HSV color space

Abstract

Tomatoes at any given time has different maturity levels, therefore it is necessary to recognize the appropriate pattern to determine the level of maturity. One of the recognition patterns of tomatoes image is to use texture and color analysis. Texture analysis can be processed using the Gray Level Co-occurrence Matrix (GLCM) method. GLCM is chosen because it has a high degree of recognition based on the value of contrast, correlation, homogeneity, and energy. Furthermore, for color analysis one of the methods that can be used is Hue, Saturation, Value (HSV). By utilizing HSV, an object with a certain color can be detected and reduce the influence of the intensity of light from outside. The results of GLCM and HSV calculations can be classified to determined the maturity level of tomatoes by using K-Nearest Neighbour (K-NN) one of basic and simple classification method which utilizes the distance (k) as a comparison of the similarity level of the image. From the research we have done, using 100 data sets, consisting of 75 training data and 25 testing data that yields the highest accuracy rate of 100% with p value on GLCM is 9 and the membership value (k) in K-NN is 3. According to the experimental results, we can conclude that our proposed method can achieve the highest accuracy.