<![CDATA[Rice variety identification based on paddy grain images faces a challenge because several varieties have relatively similar visual characteristics, particularly in shape, size, and color, which may lead to inconsistent manual identification. This condition indicates the need for a computer vision approach that can automatically learn the visual characteristics of paddy grains. This study aimed to develop a Convolutional Neural Network (CNN) model to identify five rice varieties available at the Agricultural Research and Modernization Center of Gorontalo (BRMP Gorontalo), namely Ciherang, Inpari 30, Inpari 42, Inpari IR Nutri Zinc, and Mekongga. The dataset was obtained through direct image acquisition, consisting of 28 RAW photographs for each variety and nine cropped images from each photograph, resulting in 1,260 images. The research stages included preprocessing, Custom CNN development, evaluation of image size, learning rate, number of convolutional layers, and data splitting, as well as comparison with MobileNetV2, ResNet50, DenseNet121, and InceptionV3. The best configuration used an input size of 224×224 pixels, a learning rate of 0.0005, four convolutional layers, and an 80:20 data split. Testing on 135 final testing images achieved 93.33% accuracy, 93.44% precision, 93.33% recall, and 93.32% F1-score. These results indicate that the Custom CNN effectively identified the five rice varieties under the dataset and experimental conditions used in this study.]]>
