Traditional food knowledge (TFK) is an essential aspect of human life. In terms of sociocultural aspects, TFK is necessary to protect ancestral culture. In terms of health, traditional foods contain better and more natural ingredients compared to the ingredients of processed foods. Considering this background, in this study, data acquisition and automatic food recognition were performed for traditional food in Indonesia. The food images were captured in a professional mini studio. The food image data were captured under the same light intensity, camera settings, and shooting distance from the camera. The parameters were precisely measured and configured with a light intensity meter, adjustable lighting, and a laser distance measurement device. The data of 1644 traditional food images were successfully obtained in the data acquisition process. These images corresponded to 34 types of traditional foods, and 30–50 images were obtained for each type of food. The size of the raw food image data was 53 GB. The data were divided into sets for training, testing, and validation. An automatic recognition system was developed to classify the traditional food of Indonesia. Training was performed using several types of convolutional neural network (CNN) models such as Densenet121, Resnet50, InceptionV3, and Nasnetmobile. The evaluation results indicated that when using a high quality dataset, the automatic recognition system could realize satisfactory area under the receiver operating characteristics (AUROC) and high accuracy, precision, and recall values of more than 0.95.

中文翻译：

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印度尼西亚的传统食品知识：新的高质量食品数据集和自动识别系统

传统食品知识（TFK）是人类生活的重要方面。在社会文化方面，TFK是保护祖传​​文化所必需的。在健康方面，传统食品比加工食品的成分更好，更天然。考虑到这一背景，在这项研究中，对印度尼西亚的传统食品进行了数据采集和自动食品识别。食物图像是在专业的迷你摄影棚中拍摄的。在相同的光强度，相机设置和距相机的拍摄距离下捕获食物图像数据。使用光强度计，可调照明和激光测距设备精确测量和配置参数。在数据采集过程中成功获取了1644张传统食物图像的数据。这些图像对应于34种传统食品，每种类型的食物获得了30–50张图像。生食图像数据的大小为53 GB。将数据分为几组进行训练，测试和验证。开发了自动识别系统以对印度尼西亚的传统食品进行分类。使用几种类型的卷积神经网络（CNN）模型（例如Densenet121，Resnet50，InceptionV3和Nasnetmobile）进行训练。评估结果表明，当使用高质量数据集时，该自动识别系统可以在接收器操作特性（AUROC）下实现令人满意的区域，并具有0.95以上的高精度，精度和召回值。生食图像数据的大小为53 GB。将数据分为几组进行训练，测试和验证。开发了自动识别系统以对印度尼西亚的传统食品进行分类。使用几种类型的卷积神经网络（CNN）模型（例如Densenet121，Resnet50，InceptionV3和Nasnetmobile）进行训练。评估结果表明，当使用高质量数据集时，该自动识别系统可以在接收器操作特性（AUROC）下实现令人满意的区域，并具有0.95以上的高精度，精度和召回值。生食图像数据的大小为53 GB。将数据分为几组进行训练，测试和验证。开发了自动识别系统以对印度尼西亚的传统食品进行分类。使用几种类型的卷积神经网络（CNN）模型（例如Densenet121，Resnet50，InceptionV3和Nasnetmobile）进行训练。评估结果表明，当使用高质量数据集时，该自动识别系统可以在接收器操作特性（AUROC）下实现令人满意的区域，并具有0.95以上的高精度，精度和召回值。使用几种类型的卷积神经网络（CNN）模型（例如Densenet121，Resnet50，InceptionV3和Nasnetmobile）进行训练。评估结果表明，当使用高质量数据集时，该自动识别系统可以在接收器操作特性（AUROC）下实现令人满意的区域，并具有0.95以上的高精度，精度和召回值。使用几种类型的卷积神经网络（CNN）模型（例如Densenet121，Resnet50，InceptionV3和Nasnetmobile）进行训练。评估结果表明，当使用高质量数据集时，自动识别系统可以在接收器工作特性（AUROC）下实现令人满意的区域，并且精度，精度和召回值均超过0.95。