Classification of morphologically similar Indian rice variety using machine learning algorithms

Abstrak

India, among the agriculture-based economy grows wide variety of rice along with other crops. These varieties have different commercial values as they are different in their features. It becomes extremely challenging to classify rice varieties which have similar features but are different in their quality. This study considers four varieties of similar looking rice which conform to be Sona-Masuri. A total of 4180 images are considered to extract 56 features including textural, red, green, blue (RGB) and hue, saturation, value (HSV) color and wavelet decomposition. Machine learning (ML) models like support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT) and voting classifiers are developed for feature dataset and convolutional neural network (CNN) model for image dataset. The results obtained for every model are obtained using statistical methods and the results are expressed in a table for accuracy, precision, recall and F1-score. A classification accuracy of 72.48% is obtained for SVM using polynomial kernel trick by considering all 56 features. The customized CNN model is designed with three convolution layers has resulted in 97.13% of training accuracy and 87.5% of validation accuracy. Based on the results obtained, it is witnessed that the ML models employed in this study to classify rice types with similar appearances have practical applications.