Identification of Rice Varieties Using Machine Learning Algorithms

Rice, one of the world’s most extensively produced grain crops, has numerous genetic variants. These kinds are differentiated from one another based on their characteristics. Typically, these characteristics include texture, form, and color. It is possible to classify and evaluate the quality of seeds based on these qualities. Five types of rice produced often in Turkey were used for the research: Arborio, Basmati, Ipsala, Jasmine, and Karacadag. The dataset contains a total of 7424-grain pictures. Several classification methods were applied with the pre-trained deep architectures. Using the models’ confusion matrix values, statistical results of sensitivity, specificity, prediction, F1 score, accuracy, false positive rate, and false negative rate were calculated and tabulated for each model. The classification success rates were 99.7% for VGG16 CNN with logistic regression classifier. Based on the findings, it is evident that the classification models utilized in the study for rice varieties can be successfully applied in this field.