Rice, which is among the most widely produced grain products worldwide, has many genetic varieties. These varieties are separated from each other due to some of their features. These are usually features such as texture, shape, and color. With these features that distinguish rice varieties, it is possible to classify and evaluate the quality of seeds. In this study, Arborio, Basmati, Ipsala, Jasmine and Karacadag, which are five different varieties of rice often grown in Turkey, were used. A total of 75,000 grain images, 15,000 from each of these varieties, are included in the dataset. A second dataset with 106 features including 12 morphological, 4 shape and 90 color features obtained from these images was used. Models were created by using Artificial Neural Network (ANN) and Deep Neural Network (DNN) algorithms for the feature dataset and by using the Convolutional Neural Network (CNN) algorithm for the image dataset, and classification processes were performed. Statistical results of sensitivity, specificity, prediction, F1 score, accuracy, false positive rate and false negative rate were calculated using the confusion matrix values of the models and the results of each model were given in tables. Classification successes from the models were achieved as 99.87% for ANN, 99.95% for DNN and 100% for CNN. With the results, it is seen that the models used in the study in the classification of rice varieties can be applied successfully in this field.

大米是世界上生产最广泛的谷物产品之一，具有许多遗传品种。由于它们的一些特征，这些品种彼此分开。这些通常是纹理、形状和颜色等特征。有了这些区分水稻品种的特征，就可以对种子的质量进行分类和评估。在这项研究中，使用了土耳其经常种植的五种不同的水稻品种 Arborio、Basmati、Ipsala、Jasmine 和 Karacadag。数据集中包含总共 75,000 张谷物图像，其中每个品种 15,000 张。使用了具有 106 个特征的第二个数据集，其中包括从这些图像中获得的 12 个形态特征、4 个形状特征和 90 个颜色特征。通过对特征数据集使用人工神经网络 (ANN) 和深度神经网络 (DNN) 算法以及对图像数据集使用卷积神经网络 (CNN) 算法创建模型，并执行分类过程。使用模型的混淆矩阵值计算敏感性、特异性、预测、F1评分、准确度、假阳性率和假阴性率的统计结果，并在表中给出了每个模型的结果。模型的分类成功率为 ANN 为 99.87%，DNN 为 99.95%，CNN 为 100%。从结果可以看出，在水稻品种分类研究中使用的模型可以成功地应用于该领域。并进行了分类处理。使用模型的混淆矩阵值计算敏感性、特异性、预测、F1评分、准确度、假阳性率和假阴性率的统计结果，并在表中给出了每个模型的结果。模型的分类成功率为 ANN 为 99.87%，DNN 为 99.95%，CNN 为 100%。从结果可以看出，在水稻品种分类研究中使用的模型可以成功地应用于该领域。并进行了分类处理。使用模型的混淆矩阵值计算敏感性、特异性、预测、F1评分、准确度、假阳性率和假阴性率的统计结果，并在表中给出了每个模型的结果。这些模型的分类成功率为 ANN 为 99.87%，DNN 为 99.95%，CNN 为 100%。从结果可以看出，在水稻品种分类研究中使用的模型可以成功地应用于该领域。ANN 为 87%，DNN 为 99.95%，CNN 为 100%。从结果可以看出，在水稻品种分类研究中使用的模型可以成功地应用于该领域。ANN 为 87%，DNN 为 99.95%，CNN 为 100%。从结果可以看出，在水稻品种分类研究中使用的模型可以成功地应用于该领域。