Classification of Trifolium Seeds by Computer Vision Methods

Abstract

Traditional machine learning methods have been extensively used in computer vision applications. However, recent improvements in computer technology have changed this trend. The dominance of deep learning methods in the field is observed when state-of-the-art studies are examined. This study employs traditional computer vision methods and deep learning to classify five different types of Trifolium seeds. Trifolium, the leading food for nutritious dairy products, plays an essential role in livestock in some parts of the world. First, an image data set consisting of 1903 images belonging to five different species of Trifolium was created. Descriptive and quantitative morphological features of each species are extracted using image-processing techniques. Then a feature matrix was created using eight different features. After feature selection and transformation, unnecessary and irrelevant features were removed from the data set to build more accurate and robust classification models. Four common and frequently applied classification algorithms created a prediction model in the seed data set. In addition, the same dataset was trained using VGG19, a convolutional neural network. Finally, the performance metrics of each classifier were computed and evaluated. The decision tree has the worst accuracy among the four traditional methods, 92.07%. On the other hand, Artificial Neural Network has the highest accuracy with 94.59%. As expected, VGG19 outperforms all traditional methods with 96.29% accuracy. However, as the results show, traditional methods can also produce results close to the deep learning methods.