Rice Foreign Object Classification Based on Integrated Color and Textural Feature Using Machine Learning

A blend of natural and artificial foreign objects can be used to determine the rice quality.The agricultural industry, particularly rice plants, has demonstrated great success rates for object detection based on image processing.Most food quality studies can be seen from the image shape color and size, and the rice quality can be seen from the absence of the foreign object.HSV color and GLCM texture are used to classify natural and non-natural foreign object images using the support vector machine (SVM) algorithm and other comparison methods, namely decision tree and naive Bayes.The dataset for foreign objects consists of 80 images, 20 of which are each of the following classes: stone, grain, yellow-broken, and red-black.The dataset will be preprocessed to obtain the feature values for color and texture.SVM method with cross-validation, the highest accuracy value is 96.83%, a decision tree is 87.31%, and naive Bayes is 82.54% in detecting natural and non-natural foreign objects.The use of cross-validation techniques with a value of K=5 gives an average accuracy increase of 10% compared to those without cross-validation.These results show that natural foreign objects in different classes can be appropriately detected using a combination of color and texture features in the SVM classification method and cross-validation.